Статті в журналах: "PLSDA"

1

Xu, Lu, De-Hua Deng, Chen-Bo Cai, and Hong-Wei Yang. "Automatic Discrimination of the Geographical Origins of Milks by Excitation-Emission Fluorescence Spectrometry and Chemometrics." Journal of Automated Methods and Management in Chemistry 2011 (2011): 1–6. http://dx.doi.org/10.1155/2011/323196.

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This paper presents the automatic discrimination of geographical origins of milks from Western Yunnan Plateau areas and eastern China by excitation-emission fluorescence spectrometry and chemometrics. Genuine plateau milks (n=60) and milks from eastern China (n=89) are scanned in the regions of 180–300 nm for excitation and 200–800 nm for emission. Different options of data analysis are investigated and compared in terms of their performance in discriminating milks of different geographical origins: (1) two-way partial least squares discriminant analysis (PLSDA) based on excitation and emission spectra, respectively; (2) two-way PLSDA based on fusion of excitation and emission spectra; (3) three-way PLSDA based on excitation-emission matrix spectra. The two-way PLSDA methods with excitation spectra, emission spectra, and fusion of excitation and emission spectra correctly classify 91.3%, 88.6%, and 95.3% of the milk samples, respectively; while the total accuracy of three-way PLSDA is 96.0%. The results demonstrate the two-way data combining excitation and emission spectra are sufficient to characterize and identify the plateau milks. Considering both model accuracy and the analytical time required, two-way PLS-DA with fusion of excitation and emission spectra is recommended as a reliable and quick method to discriminate plateau milks from ordinary milks.

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2

Westerhuis, Johan A., Huub C. J. Hoefsloot, Suzanne Smit, Daniel J. Vis, Age K. Smilde, Ewoud J. J. van Velzen, John P. M. van Duijnhoven, and Ferdi A. van Dorsten. "Assessment of PLSDA cross validation." Metabolomics 4, no. 1 (January 24, 2008): 81–89. http://dx.doi.org/10.1007/s11306-007-0099-6.

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3

Yan, Si-Min, Zi-Feng Hu, Cheng-Xin Wu, Lu Jin, Gong Chen, Xian-Yu Zeng, and Jia-Qi Zhu. "Electronic Tongue Combined with Chemometrics to Provenance Discrimination for a Green Tea (Anji-White Tea)." Journal of Food Quality 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/3573197.

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This paper aims to provide a stable instrumental method for provenance discrimination of Anji-White tea by its distinctive taste. 180 authentic and 60 counterfeit white tea samples were collected for specific geographical origins detection; all of them were measured by electronic tongue coupled with 7 independent sensors. Therefore, chemometrics methods, principal component analysis (PCA), and partial least squares discriminant analysis (PLSDA) were performed in classification. The PCA distribution shows that, in provenance analysis, PCA is a simple and reliable tool for small sample sets, but for sets with large objects, PCA seems powerless in classification. Therefore, PLSDA was applied to develop a classification model. The prediction sensitivity and specificity of PLSDA, respectively, reached 0.917 and 0.950. This study demonstrates the potential of combining electronic tongue system and chemometrics as an effective tool for specific geographical origins detection in Anji-White tea.

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4

Xu, Lu, Qiong Shi, Si-Min Yan, Hai-Yan Fu, Shunping Xie, and Daowang Lu. "Chemometric Analysis of Elemental Fingerprints for GE Authentication of Multiple Geographical Origins." Journal of Analytical Methods in Chemistry 2019 (July 11, 2019): 1–7. http://dx.doi.org/10.1155/2019/2796502.

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The feasibility of combining elemental fingerprints and chemical pattern recognition methods for authentication of the geographical origins of a Chinese herb, Gastrodia elata BI. (GE), was studied in this paper. A total of 210 GE samples were collected from 7 different producing areas. The levels of 15 mineral elements in GE, including Zn, Cd, Co, Cr, Cu, Ca, Mg, Mn, Mo, Ni, Pb, Sr, Fe, Na, and K, were determined using inductively coupled plasma mass spectrometry (ICP-MS). Using the autoscaled data of elemental fingerprints and partial least-squares discriminant analysis (PLSDA), two chemometrics strategies for multiclass classifications, One-Versus-Rest (OVR) and One-Versus-One (OVO), were studied and compared in discrimination of GE geographical origins. As a result, OVR-PLSDA and OVO-PLSDA could achieve the classification accuracy of 0.672 and 0.925, respectively. The results indicate that mineral elemental fingerprints coupled with chemometrics can provide a useful alternative method for simultaneous discrimination of multiple GE geographical origins.

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5

Fu, Hai-Yan, Shuang-Yan Huan, Lu Xu, Li-Juan Tang, Jian-Hui Jiang, Hai-Long Wu, Guo-Li Shen, and Ru-Qin Yu. "Moving Window Partial Least-Squares Discriminant Analysis for Identification of Different Kinds of Bezoar Samples by near Infrared Spectroscopy and Comparison of Different Pattern Recognition Methods." Journal of Near Infrared Spectroscopy 15, no. 5 (October 2007): 291–97. http://dx.doi.org/10.1255/jnirs.743.

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Moving window partial least-squares (MWPLS) regression was coupled with near infrared (NIR) spectra as an interval selection method to improve the performance of partial least squares discriminant analysis (PLSDA) models. This method was applied to the identification of artificial bezoar, natural bezoar and artificial bezoar in natural bezoar and compared with some traditional pattern recognition methods, such as principal component analysis (PCA), linear discriminant analysis (LDA) and PLSDA. The introduction of MWPLS enhanced the performance of PLSDA model. The results obtained showed that moving window partial least-squares discriminant analysis (MWPLSDA) can extract wavelength intervals with useful information and build simple yet effective classification models that can significantly improve the classification accuracy. Then MWPLSDA was used to identify natural bezoar by geographical origin; a promising result was achieved. The work showed that MWPLSDA could be a promising method for quality analysis and discrimination of chinese medical herbs according to geographical origin.

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6

Fu, Haiyan, Yao Fan, Xu Zhang, Hanyue Lan, Tianming Yang, Mei Shao, and Sihan Li. "Rapid Discrimination for Traditional Complex Herbal Medicines from Different Parts, Collection Time, and Origins Using High-Performance Liquid Chromatography and Near-Infrared Spectral Fingerprints with Aid of Pattern Recognition Methods." Journal of Analytical Methods in Chemistry 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/727589.

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As an effective method, the fingerprint technique, which emphasized the whole compositions of samples, has already been used in various fields, especially in identifying and assessing the quality of herbal medicines. High-performance liquid chromatography (HPLC) and near-infrared (NIR), with their unique characteristics of reliability, versatility, precision, and simple measurement, played an important role among all the fingerprint techniques. In this paper, a supervised pattern recognition method based on PLSDA algorithm by HPLC and NIR has been established to identify the information ofHibiscus mutabilisL. andBerberidis radix, two common kinds of herbal medicines. By comparing component analysis (PCA), linear discriminant analysis (LDA), and particularly partial least squares discriminant analysis (PLSDA) with different fingerprint preprocessing of NIR spectra variables, PLSDA model showed perfect functions on the analysis of samples as well as chromatograms. Most important, this pattern recognition method by HPLC and NIR can be used to identify different collection parts, collection time, and different origins or various species belonging to the same genera of herbal medicines which proved to be a promising approach for the identification of complex information of herbal medicines.

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7

Ma, Yue, Yichao Xu, Hui Yan, and Guozheng Zhang. "On-line identification of silkworm pupae gender by short-wavelength near infrared spectroscopy and pattern recognition technology." Journal of Near Infrared Spectroscopy 29, no. 4 (April 15, 2021): 207–15. http://dx.doi.org/10.1177/0967033521999745.

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The gender identification of silkworm pupae is a critical step in the sericulture industry's breeding process. In this study, a low cost, short-wavelength (815-1075 nm) near infrared (NIR) spectrometer combined with multivariate spectra evaluation methods was used to establish calibration models for the on-line identification of female and male pupae of eight silkworm varieties. The diffuse reflection short-wavelength spectra were recorded, and then principal component analysis (PCA), linear discriminant analysis (LDA), and partial least squares discriminant analysis (PLSDA) were tested for calibration model development. The PCA and LDA results showed, that spectral differences between the female and male silkworm pupae existed, however, the two evaluation techniques could not separate the female and male silkworm pupae with the required accuracy. The PLSDA calibration models, on the other hand, could separate the pupae according to their gender with the necessary prediction accuracy of >98%. Thus, it has been proved, that a low-cost, short-wavelength range NIR spectrometer in combination with a PLSDA calibration routine can be successfully applied for the reliable on-line identification of female and male silkworm pupae.

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8

Xu, Lu, Si-Min Yan, Chen-Bo Cai, and Xiao-Ping Yu. "Nondestructive Discrimination of Lead (Pb) in Preserved Eggs (Pidan) by Near-Infrared Spectroscopy and Chemometrics." Journal of Spectroscopy 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/253143.

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A major safety concern with pidan (preserved eggs) has been the usage of lead (II) oxide (PbO) during its processing. This paper develops a rapid and nondestructive method for discrimination of lead (Pb) in preserved eggs with different processing methods by near-infrared (NIR) spectroscopy and chemometrics. Ten batches of 331 unleaded eggs and six batches of 147 eggs processed with usage of PbO were collected and analyzed by NIR spectroscopy. Inductively coupled plasma mass spectrometry (ICP-MS) analysis was used as a reference method for Pb identification. The Pb contents of leaded eggs ranged from 1.2 to 12.8 ppm. Linear partial least squares discriminant analysis (PLSDA) and nonlinear least squares support vector machine (LS-SVM) were used to classify samples based on NIR spectra. Different preprocessing methods were studied to improve the classification performance. With second-order derivative spectra, PLSDA and LS-SVM obtained accurate and reliable classification of leaded and unleaded preserved eggs. The sensitivity and specificity of PLSDA were 0.975 and 1.000, respectively. Because the strictest safety standard of Pb content in traditional pidan is 2 ppm, the proposed method shows the feasibility for rapid and nondestructive discrimination of Pb in Chinese preserved eggs.

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9

Valori, Roberto, Corrado Costa, Simone Figorilli, Luciano Ortenzi, Rossella Manganiello, Roberto Ciccoritti, Francesca Cecchini, et al. "Advanced Forecasting Modeling to Early Predict Powdery Mildew First Appearance in Different Vines Cultivars." Sustainability 15, no. 3 (February 3, 2023): 2837. http://dx.doi.org/10.3390/su15032837.

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Eurasian grapevine is a widely cultivated horticultural plant worldwide, but it is more susceptible to powdery mildew. In recent years, the high cost and negative environmental impact of calendar-applied sulfur fungicides are leading research to find alternative remedies. In this study, the early prediction (three days) of the first appearance of powdery mildew infection, on two different Italian grapevine cultivars, was detected through a partial least squares discriminant analysis (PLSDA). The treatment indications of the “PLSDA” models (treatments according to the predictive model) were compared with those of the “Standard” (treatments according to the established agricultural practice of the area). This allowed the early containment of the disease, preventing its subsequent propagation. The model was built based on weather-climate data and phytopathological information collected on the “Untreated” control cultivar to monitor the natural spread of the disease (three years of training and two of tests). For both the cultivars and the two test years (2021 and 2022), the “PLSDA” models early predicted the first appearance of fungal disease, reducing the treatment number (about four) with respect to “Standard”. In addition, analyses of key fruit quality parameters were conducted to evaluate the effectiveness of treatment reduction.

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10

Fu, Haiyan, Qiong Shi, Liuna Wei, Lu Xu, Xiaoming Guo, Ou Hu, Wei Lan, Shunping Xie, and Tianming Yang. "Rapid Recognition of Geoherbalism and Authenticity of a Chinese Herb by Data Fusion of Near-Infrared Spectroscopy (NIR) and Mid-Infrared (MIR) Spectroscopy Combined with Chemometrics." Journal of Spectroscopy 2019 (April 30, 2019): 1–9. http://dx.doi.org/10.1155/2019/2467185.

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Fourier transform near-infrared (NIR) spectroscopy and mid-infrared (MIR) spectroscopy play important roles in all fingerprint techniques because of their unique characteristics such as reliability, versatility, precision, and ease of measurement. In this paper, a supervised pattern recognition method based on the PLSDA algorithm by NIR and the NIR-MIR fusion spectra has been established to identify geoherbalism of Angelica dahurica from different regions and authenticity of Corydalis yanhusuo W. T. Wang. Comparing principle component analysis (PCA) cannot successfully identify geographical origins of Angelica dahurica. Linear discriminant analysis (LDA) also hardly distinguishes those origins. Furthermore, the PLSDA model based on the data fusion of NIR and IR was more accurate and efficient. But, the identification of authenticity of Corydalis yanhusuo W. T. Wang was still inaccurate in the PLSDA model. Consequently, data fusion of NIR-MIR original spectra combined with moving window partial least-squares discriminant analysis was firstly used and showed perfect properties on authenticity and adulteration discrimination of Corydalis yanhusuo W. T. Wang. It indicated that data fusion of NIR-MIR spectra combined with MWPLSDA could be considered as the promising tool for rapid discrimination of the geoherbalism and authenticity of more Chinese herbs in the future.

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11

Guo, Pingping, Junsong Wang, Ge Dong, Dandan Wei, Minghui Li, Minghua Yang, and Lingyi Kong. "NMR-based metabolomics approach to study the chronic toxicity of crude ricin from castor bean kernels on rats." Mol. BioSyst. 10, no. 9 (2014): 2426–40. http://dx.doi.org/10.1039/c4mb00251b.

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12

Xu, Lu, Si-Min Yan, Zi-Hong Ye, Xian-Shu Fu, and Xiao-Ping Yu. "Combining Electronic Tongue Array and Chemometrics for Discriminating the Specific Geographical Origins of Green Tea." Journal of Analytical Methods in Chemistry 2013 (2013): 1–5. http://dx.doi.org/10.1155/2013/350801.

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The feasibility of electronic tongue and multivariate analysis was investigated for discriminating the specific geographical origins of a Chinese green tea with Protected Designation of Origin (PDO). 155 Longjing tea samples from three subareas were collected and analyzed by an electronic tongue array of 7 sensors. To remove the influence of abnormal measurements and samples, robust principal component analysis (ROBPCA) was used to detect outliers in each class. Partial least squares discriminant analysis (PLSDA) was then used to develop a classification model. The prediction sensitivity/specificity of PLSDA was 1.000/1.000, 1.000/0.967, and 0.950/1.000 for longjing from Xihu, Qiantang, and Yuezhou, respectively. Electronic tongue and chemometrics can provide a rapid and reliable tool for discriminating the specific producing areas of Longjing.

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13

Westerhuis, Johan A., Ewoud J. J. van Velzen, Huub C. J. Hoefsloot, and Age K. Smilde. "Multivariate paired data analysis: multilevel PLSDA versus OPLSDA." Metabolomics 6, no. 1 (October 28, 2009): 119–28. http://dx.doi.org/10.1007/s11306-009-0185-z.

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14

Chu, Xuan, Pu Miao, Kun Zhang, Hongyu Wei, Han Fu, Hongli Liu, Hongzhe Jiang, and Zhiyu Ma. "Green Banana Maturity Classification and Quality Evaluation Using Hyperspectral Imaging." Agriculture 12, no. 4 (April 8, 2022): 530. http://dx.doi.org/10.3390/agriculture12040530.

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Physiological maturity of bananas is of vital importance in determination of their quality and marketability. This study assessed, with the use of a Vis/NIR hyperspectral imaging (400–1000 nm), the feasibility in differentiating six maturity levels (maturity level 2, 4, and 6 to 9) of green dwarf banana and characterizing their quality changes during maturation. Spectra were extracted from three zones (pedicel, middle and apex zone) of each banana finger, respectively. Based on spectra of each zone, maturity identification models with high accuracy (all over 91.53% in validation set) were established by partial least squares discrimination analysis (PLSDA) method with raw spectra. A further generic PLSDA model with an accuracy of 94.35% for validation was created by the three zones’ spectra pooled to omit the effect of spectra acquisition position. Additionally, a spectral interval was selected to simplify the generic PLSDA model, and an interval PLSDA model was built with an accuracy of 85.31% in the validation set. For characterizing some main quality parameters (soluble solid content, SSC; total acid content, TA; chlorophyll content and total chromatism, ΔE*) of banana, full-spectra partial least squares (PLS) models and interval PLS models were, respectively, developed to correlate those parameters with spectral data. In full-spectra PLS models, high coefficients of determination (R2) were 0.74 for SSC, 0.68 for TA, and fair of 0.42 as well as 0.44 for chlorophyll and ΔE*. The performance of interval PLS models was slightly inferior to that of the full-spectra PLS models. Results suggested that models for SSC and TA had an acceptable predictive ability (R2 = 0.64 and 0.59); and models for chlorophyll and ΔE* (R2 = 0.34 and 0.30) could just be used for sample screening. Visualization maps of those quality parameters were also created by applying the interval PLS models on each pixel of the hyperspectral image, the distribution of quality parameters in which were basically consistent with the actual measurement. This study proved that the hyperspectral imaging is a useful tool to assess the maturity level and quality of dwarf bananas.

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15

Xu, Lu, Hai-Yan Fu, Chen-Bo Cai, and Yuan-Bin She. "Quality Degradation of Chinese White Lotus Seeds Caused by Dampening during Processing and Storage: Rapid and Nondestructive Discrimination Using Near-Infrared Spectroscopy." Journal of Analytical Methods in Chemistry 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/345352.

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Dampening during processing or storage can largely influence the quality of white lotus seeds (WLS). This paper investigated the feasibility of using near-infrared (NIR) spectroscopy and chemometrics for rapid and nondestructive discrimination of the dampened WLS. Regular (n=167) and dampened (n=118) WLS objects were collected from five main producing areas and NIR reflectance spectra (4000–12000 cm−1) were measured for bare kernels. The influence of spectral preprocessing methods, including smoothing, taking second-order derivatives (D2), and standard normal variate (SNV), on partial least squares discrimination analysis (PLSDA) was compared to select the optimal data preprocessing method. A moving-window strategy was combined with PLSDA (MWPLSDA) to select the most informative wavelength intervals for classification. Based on the selected spectral ranges, the sensitivity, specificity, and accuracy were 0.927, 0.950, and 0.937 for SNV-MWPLSDA, respectively.

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16

Raimondo, Mariangela, Anna Borioni, Francesca Prestinaci, Isabella Sestili, and Maria Cristina Gaudiano. "A NIR, ¹H-NMR, LC-MS and chemometrics pilot study on the origin of carvedilol drug substances: a tool for discovering falsified active pharmaceutical ingredients." Analytical Methods 14, no. 14 (2022): 1396–405. http://dx.doi.org/10.1039/d1ay02035h.

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The study explores the profile of carvedilol active ingredients by NIR, 1H-NMR and LC-MS Q-TOF and data were analysed by PCA, cluster analysis and PLSDA. Two different groups of manufacturers based on the geographical area are classified.

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17

Song, Juan, Qiong Shi, Si-Min Yan, Hai-Yan Fu, Si-Zhan Wu, and Lu Xu. "Classification of Different Blueberry Cultivars by Analysis of Physical Factors, Chemical and Nutritional Ingredients, and Antioxidant Capacities." Journal of Food Quality 2020 (September 26, 2020): 1–9. http://dx.doi.org/10.1155/2020/9474158.

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Blueberry fruits of different cultivars are featured with different quality indices. In this work, three types of quality factors, including 6 physical parameters, 12 chemical and nutritional components, and 3 antioxidant indices, were measured to compare and classify blueberry fruits from 12 different cultivars in China. Using the autoscaled data of quality factors, unsupervised principal component analysis was performed for exploratory analysis of intercultivar differences and the influences of quality factors. A supervised classification method, partial least squares discriminant analysis (PLSDA), was combined with the global particle swarm optimization algorithm (PSO) and two multiclass strategies, one-versus-rest (OVR) and one-versus-one (OVO), to select discriminative quality factors and develop classification models of the 12 cultivars. As a result, OVO-PLSDA with 8 quality factors could achieve the classification accuracy of 0.915. This study will provide new insights into the quality variations and key factors among different blueberry cultivars.

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18

Bonifazi, Giuseppe, Giuseppe Capobianco, Paola Cucuzza, Silvia Serranti, and Andrea Uzzo. "Recycling-oriented characterization of PET waste stream by SWIR hyperspectral imaging and variable selection methods." Volume 18 - March 2022, no. 18 (March 31, 2022): 42–49. http://dx.doi.org/10.31025/2611-4135/2022.15168.

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The proposed study was carried out to develop a fast and efficient strategy for plastic waste sensor-based sorting in recycling plants, based on hyperspectral imaging (HSI), combined with variable selection methods, to produce a high-quality recycled polyethylene terephthalate (PET) flakes stream. Variable selection techniques were applied in order to identify a limited number of spectral bands useful to recognize the presence of other plastic materials, considered as contaminant, inside a stream of recycled PET flakes, reducing processing time as requested by sorting online applications. Post-consumer plastic samples were acquired by HSI device working in the short-wave infrared (SWIR) range (1000 - 2500 nm). As a first step, the hypercubes were processed applying chemometric logics to build a partial least squares discriminant analysis (PLSDA) classification model using the full investigated spectral range, able to identify PET and contaminant classes. As a second step, two different variable selection methods were then applied, i.e., interval PLSDA (I-PLSDA) and variable importance in projection (VIP) scores, in order to identify a limited number of spectral bands useful to recognize the two classes and to evaluate the best method, showing efficiency values close to those obtained by the full spectrum model. The best result was achieved by the VIP score method with an average efficiency value of 0.98. The obtained results suggested that the variables selection method can represent a powerful approach for the sensor-based sorting-online, decreasing the amount of data to be processed and thus enabling faster recognition compared to the full spectrum model.

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19

Westerhuis, Johan A., Ewoud J. J. van Velzen, Huub C. J. Hoefsloot, and Age K. Smilde. "Discriminant Q2 (DQ2) for improved discrimination in PLSDA models." Metabolomics 4, no. 4 (August 30, 2008): 293–96. http://dx.doi.org/10.1007/s11306-008-0126-2.

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20

Angelini, Claudio, Carlo Utzeri, Corrado Costa, Paolo Menesatti, and Stefano Raimondi. "Image analysis of the ventral colour pattern discriminates between Spectacled salamanders, Salamandrina perspicillata and S. terdigitata (Amphibia, Salamandridae)." Amphibia-Reptilia 31, no. 2 (2010): 273–82. http://dx.doi.org/10.1163/156853810791069047.

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AbstractIn the present study, we applied statistical methods to quantitative image analysis of the persistent and individual ventral colour pattern of Salamandrina salamanders, in order to discriminate between individuals of the two species belonging to this genus. Pictures of 238 individuals from three populations of S. perspicillata and pictures of 95 S. terdigitata from two populations were analysed. Partial least squares discriminant analysis (PLSDA) classified 98.78% of individuals into the correct species. PLSDA reaches lower percentages of correct classification when applied to discriminate individuals from different populations of the same species (74.14% for S. perspicillata, 78.26% for S. terdigitata). An ANOVA analysis of colour abundances in different body sectors reveals significant differences between species. The results show that colour pattern has a specific basis, the most discriminant areas being the head and the pectoral girdle. We discuss these results in the light of the proposed evolutionary scenarios of the species, and suggest that ventral colour patterns were driven by founder effect.

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21

Wang, Rong, Aparna Naidu, and Yong Wang. "Oral Cancer Discrimination and Novel Oral Epithelial Dysplasia Stratification Using FTIR Imaging and Machine Learning." Diagnostics 11, no. 11 (November 17, 2021): 2133. http://dx.doi.org/10.3390/diagnostics11112133.

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The Fourier transform infrared (FTIR) imaging technique was used in a transmission model for the evaluation of twelve oral hyperkeratosis (HK), eleven oral epithelial dysplasia (OED), and eleven oral squamous cell carcinoma (OSCC) biopsy samples in the fingerprint region of 1800–950 cm−1. A series of 100 µm × 100 µm FTIR imaging areas were defined in each sample section in reference to the hematoxylin and eosin staining image of an adjacent section of the same sample. After outlier removal, signal preprocessing, and cluster analysis, a representative spectrum was generated for only the epithelial tissue in each area. Two representative spectra were selected from each sample to reflect intra-sample heterogeneity, which resulted in a total of 68 representative spectra from 34 samples for further analysis. Exploratory analyses using Principal component analysis and hierarchical cluster analysis showed good separation between the HK and OSCC spectra and overlaps of OED spectra with either HK or OSCC spectra. Three machine learning discriminant models based on partial least squares discriminant analysis (PLSDA), support vector machines discriminant analysis (SVMDA), and extreme gradient boosting discriminant analysis (XGBDA) were trained using 46 representative spectra from 12 HK and 11 OSCC samples. The PLSDA model achieved 100% sensitivity and 100% specificity, while both SVM and XGBDA models generated 95% sensitivity and 96% specificity, respectively. The PLSDA discriminant model was further used to classify the 11 OED samples into HK-grade (6), OSCC-grade (4), or borderline case (1) based on their FTIR spectral similarity to either HK or OSCC cases, providing a potential risk stratification strategy for the precancerous OED samples. The results of the current study support the application of the FTIR-machine learning technique in early oral cancer detection.

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22

Lu, Zhongying, Chengying Hai, Simin Yan, Lu Xu, Daowang Lu, Yixin Sou, Hengye Chen, Xiaolong Yang, Haiyan Fu, and Jian Yang. "Chemistry Combining Elemental Profile, Stable Isotopic Ratios, and Chemometrics for Fine Classification of a Chinese Herb Licorice (Glycyrrhiza uralensis Fisch.) from 37 Producing Area." Journal of Analytical Methods in Chemistry 2022 (August 18, 2022): 1–9. http://dx.doi.org/10.1155/2022/8906305.

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A method based on elemental fingerprint, stable isotopic analysis and combined with chemometrics was proposed to trace the geographical origins of Licorice (Glycyrrhiza uralensis Fisch) from 37 producing areas. For elemental fingerprint, the levels of 15 elements, including Ca, Cu, Mg, Pb, Zn, Sr, Mn, Se, Cd, Fe, Na, Al, Cr, Co, and K, were analyzed by inductively coupled plasma atomic emission spectrometry (ICP-AES). Three stable isotopes, including δ13C, δ15N, and δ18O, were measured using an isotope-ratio mass spectrometer (IRMS). For fine classification, three multiclass strategies, including the traditional one-versus-rest (OVR) and one-versus-one (OVO) strategies and a new ensemble strategy (ES), were combined with two binary classifiers, partial least squares discriminant analysis (PLSDA) and least squares support vector machines (LS-SVM). As a result, ES-PLSDA and ES-LS-SVM achieved 0.929 and 0.921 classification accuracy of GUF samples from the 37 origins. The results show that element fingerprint and stable isotope combined with chemometrics is an effective method for GUF traceability and provides a new idea for the geographical traceability of Chinese herbal medicine.

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23

Fu, Xian-Shu, Lu Xu, Xiao-Ping Yu, Zi-Hong Ye, and Hai-Feng Cui. "Robust and Automated Internal Quality Grading of a Chinese Green Tea (Longjing) by Near-Infrared Spectroscopy and Chemometrics." Journal of Spectroscopy 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/139347.

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Near-infrared (NIR) spectroscopy and chemometric methods were applied to internal quality control of a Chinese green tea, Longjing, with Protected Geographical Indication (PGI). A total of 2745 authentic Longjing tea samples of three different grades were analyzed by NIR spectroscopy. To remove the influence of abnormal samples, The Stahel-Donoho estimate (SDE) of outlyingness was used for outlier analysis. Partial least squares discriminant analysis (PLSDA) was then used to classify the grades of tea based on NIR spectra. Different data preprocessing methods, including smoothing, taking second-order derivative (D2) spectra, and standard normal variate (SNV) transformation, were performed to reduce unwanted spectral variations in samples of the same grade before classification models were developed. The results demonstrate that smoothing, taking D2 spectra, and SNV can improve the performance of PLSDA models. With SNV spectra, the model sensitivity was 1.000, 0.955, and 0.924, and the model specificity was 0.979, 0.952, and 0.996 for samples of three grades, respectively. FT-NIR spectrometry and chemometrics can provide a robust and effective tool for rapid internal quality control of Longjing green tea.

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Teixeira, A. Margarida, Alexandr Nemec, and Clara Sousa. "Differentiation of Taxonomically Closely Related Species of the Genus Acinetobacter Using Raman Spectroscopy and Chemometrics." Molecules 24, no. 1 (January 4, 2019): 168. http://dx.doi.org/10.3390/molecules24010168.

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In recent years, several efforts have been made to develop quick and low cost bacterial identification methods. Genotypic methods, despite their accuracy, are laborious and time consuming, leaving spectroscopic methods as a potential alternative. Mass and infrared spectroscopy are among the most reconnoitered techniques for this purpose, with Raman having been practically unexplored. Some species of the bacterial genus Acinetobacter are recognized as etiological agents of nosocomial infections associated with high rates of mortality and morbidity, which makes their accurate identification important. The goal of this study was to assess the ability of Raman spectroscopy to discriminate between 16 Acinetobacter species belonging to two phylogroups containing taxonomically closely related species, that is, the Acinetobacter baumannii-Acinetobacter calcoaceticus complex (six species) and haemolytic clade (10 species). Bacterial spectra were acquired without the need for any sample pre-treatment and were further analyzed with multivariate data analysis, namely partial least squares discriminant analysis (PLSDA). Species discrimination was achieved through a series of sequential PLSDA models, with the percentage of correct species assignments ranging from 72.1% to 98.7%. The obtained results suggest that Raman spectroscopy is a promising alternative for identification of Acinetobacter species.

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Happyana, Nizar, and Oliver Kayser. "Metabolic Changes in the Trichomes of Cannabis sativa var. bedrobinol Analyzed by ¹H-NMR-Based Metabolomics." Indonesian Journal of Chemistry 20, no. 6 (March 17, 2020): 1246. http://dx.doi.org/10.22146/ijc.48765.

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Trichomes of Cannabis sativa are the main tissue for synthesizing and storing cannabinoids, the most interesting compounds in this plant. In this report, metabolic changes in the trichomes of C. sativa var. bedrobinol were investigated by 1H-NMR-based metabolomics over the flowering session. Three cannabinoids, including Δ9-tetrahydrocannabinolic acid (THCA), cannabichromenic acid (CBCA), and Δ9-tetrahydrocannabinol (THC), were successfully identified in the chloroform extracts of the Cannabis trichomes. Meanwhile, 20 non-cannabinoid compounds, including sugars, amino acids, and other acidic constituents, were detected in the water extracts. Metabolic changes of the Cannabis trichomes during the monitoring time were successfully revealed using the models of partial least squares discriminant analysis (PLSDA) and 1H-NMR quantitative analysis. Score plots of the PLSDA models classified metabolomes based on the harvest time. Discriminant metabolites for the differentiation were detected in the loading plots of the models. THCA was found as an important discriminant compound in the chloroform extracts, while all quantified water-soluble compounds were detected, contributing to the metabolic changes of the water extracts. The obtained results shed more light on the biosynthesis of metabolites in the Cannabis trichomes over the flowering season.

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Mudasir Majeed, Mudasir Majeed, Abdullah Ijaz Hussain Abdullah Ijaz Hussain, Shahzad Ali Shahid Chatha Shahzad Ali Shahid Chatha, and Ghulam Mustafa Kamal and Qasim Ali Ghulam Mustafa Kamal and Qasim Ali. "Discrimination of Mungbean Cultivars/Varieties Based on Minor Saccharides Composition by HPLC Coupled with Multivariate Statistical Analysis." Journal of the chemical society of pakistan 42, no. 3 (2020): 418. http://dx.doi.org/10.52568/000643.

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Present study reports the potential use of HPLC coupled with principle component analysis (PCA) and partial least squares discriminant analysis (PLSDA), for differentiation of approved mungbean variety from the promising lines based on minor saccharides profiles. A total of 48 mungbean samples from one approved variety and seven promising lines were analyzed for minor saccharides using HPLC and multivariate statistical analysis. PCA showed a clear separation among the classes. PLSDA was conducted to extract the variables that were responsible for the separation of mungbean approved variety from the lines. Maltoheptaose, maltohexaose, maltopentaose, maltotretraose, maltitol, maltose, mannitole, betaine varied significantly while stachyose, raffinose, sucrose, lectitol, dulcitol, xylitol, galactose showed non-significant differences. Maltoheptaose, maltohexaose, maltotretraose, maltitol, mannitole and galactose were found as the most abundant compounds while stachyose, raffinose, sucrose, lectitol and betaine were found less abundant in all lines and approved variety of V. radiata. The study highlights metabolic variation among mungbean variety and lines for minor saccharides profiles and its usefulness for consumers to choose for their desired variety or line as well as for breeders to look into the genetic factors responsible for this variation.

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Mudasir Majeed, Mudasir Majeed, Abdullah Ijaz Hussain Abdullah Ijaz Hussain, Shahzad Ali Shahid Chatha Shahzad Ali Shahid Chatha, and Ghulam Mustafa Kamal and Qasim Ali Ghulam Mustafa Kamal and Qasim Ali. "Discrimination of Mungbean Cultivars/Varieties Based on Minor Saccharides Composition by HPLC Coupled with Multivariate Statistical Analysis." Journal of the chemical society of pakistan 42, no. 3 (2020): 418. http://dx.doi.org/10.52568/000643/jcsp/42.03.2020.

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Анотація:

Present study reports the potential use of HPLC coupled with principle component analysis (PCA) and partial least squares discriminant analysis (PLSDA), for differentiation of approved mungbean variety from the promising lines based on minor saccharides profiles. A total of 48 mungbean samples from one approved variety and seven promising lines were analyzed for minor saccharides using HPLC and multivariate statistical analysis. PCA showed a clear separation among the classes. PLSDA was conducted to extract the variables that were responsible for the separation of mungbean approved variety from the lines. Maltoheptaose, maltohexaose, maltopentaose, maltotretraose, maltitol, maltose, mannitole, betaine varied significantly while stachyose, raffinose, sucrose, lectitol, dulcitol, xylitol, galactose showed non-significant differences. Maltoheptaose, maltohexaose, maltotretraose, maltitol, mannitole and galactose were found as the most abundant compounds while stachyose, raffinose, sucrose, lectitol and betaine were found less abundant in all lines and approved variety of V. radiata. The study highlights metabolic variation among mungbean variety and lines for minor saccharides profiles and its usefulness for consumers to choose for their desired variety or line as well as for breeders to look into the genetic factors responsible for this variation.

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Lv, Chaogeng, Yali He, Chuanzhi Kang, Li Zhou, Tielin Wang, Jian Yang, and Lanping Guo. "Tracing the Geographical Origins of Dendrobe (Dendrobium spp.) by Near-Infrared Spectroscopy Sensor Combined with Porphyrin and Chemometrics." Journal of Analytical Methods in Chemistry 2020 (September 12, 2020): 1–8. http://dx.doi.org/10.1155/2020/8879957.

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Dendrobe (Dendrobium spp.) is a traditional medicinal and edible food, which is rich in nutrients and contains biologically active metabolites. The quality and price of dendrobe are related to its geographical origins, and high quality dendrobe is often imitated by low quality dendrobe in the market. In this work, near-infrared (NIR) spectroscopy sensor combined with porphyrin and chemometrics was used to distinguish 360 dendrobe samples from twelve different geographical origins. Partial least squares discriminant analysis (PLSDA) was used to study the sensing performance of traditional NIR and tera-(4-methoxyphenyl)-porphyrin (TMPP)-NIR on the identification of dendrobe origin. In the PLSDA model, the recognition rate of the training and prediction set of the TMPP-NIR could reach 100%, which was higher than the 91.85% and 91.34% of traditional NIR. And the accuracy, sensitivity, and specificity of the TMPP-NIR sensor are all 1.00. The mechanism of TMPP improving the specificity of NIR spectroscopy should be related to the π-π conjugated system and the methoxy groups of TMPP interact with the chemical components of dendrobe. This study reflected that NIR spectrum with TMPP sensor was an effective approach for identifying the geographic origin of dendrobe.

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Xu, Lu, Xian-Shu Fu, Chen-Bo Cai, and Yuan-Bin She. "The Feasibility of Using Near Infrared Spectroscopy for Rapid Discrimination of Aged Shiitake Mushroom (Lentinula edodes) after Long-Term Storage." Journal of Chemistry 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/692983.

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Long-term storage can largely degrade the taste and quality of dried shiitake mushroom (Lentinula edodes). This paper aimed at developing a rapid method for discrimination of the regular and aged shiitake by near infrared (NIR) spectroscopic analysis and chemometrics. Regular (n=197) and aged (n=133) samples of shiitake were collected from six main producing areas in two successive years (2013 and 2014). NIR reflectance spectra (4000–12000 cm−1) were measured with finely ground powders. Different data preprocessing method including smoothing, taking second-order derivatives (D2), and standard normal variate (SNV) were investigated to reduce the unwanted spectral variations. Partial least squares discriminant analysis (PLSDA) and least squares support vector machine (LS-SVM) were used to develop classification models. The results indicate that SNV and D2 can largely enhance the classification accuracy. The best sensitivity, specificity, and accuracy of classification were 0.967, 0.953, and 0.961 obtained by SNV-LS-SVM and 0.933, 0.930, and 0.932 obtained by SNV-PLSDA, respectively. Moreover, the low model complexity and the high accuracy in predicting objects produced in different years demonstrate that the classification models had a good generalization performance.

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Hu, Xiaowen, Lingjie Yang, Zuxin Zhang, and Yanrong Wang. "Differentiation of alfalfa and sweet clover seeds via multispectral imaging." Seed Science and Technology 48, no. 1 (April 30, 2020): 83–99. http://dx.doi.org/10.15258/sst.2020.48.1.11.

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It is hard to remove sweet clover seeds from alfalfa seed lots by conventional methods, affecting the purity of seed lots and resulting losses in for alfalfa hay production as well as seed yield. However, the discrimination of sweet clover seed contaminates in alfalfa seed lots is difficult without special training. In this study, multispectral imaging with object-wise multivariate image analysis was evaluated for its potential to separate sweet clover and alfalfa seeds. Principal component analysis (PCA), linear discrimination analysis (LDA), partial least squares discriminant analysis (PLSDA), AdaBoost and support vector machine (SVM) methods were applied to classify seeds of sweet clover and alfalfa according to their morphological features and spectral traits or a combination thereof. The results showed that an excellent classification could be achieved based on a combination of morphological features and spectral data in a tested data set. Seed classification accuracy was up to 99.58% in a validation set with the LDA model, which was better than the PLSDA (68.19%), AdaBoost (96.95%) and SVM (98.47%) models. Thus, multispectral imaging together with chemometric multivariate analysis is a promising technique to identify sweet clover seeds in alfalfa seed lots with high efficiency.

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Defnet, Peter A., Michael A. Wise, Russell S. Harmon, Richard R. Hark, and Keith Hilferding. "Analysis of Garnet by Laser-Induced Breakdown Spectroscopy—Two Practical Applications." Minerals 11, no. 7 (June 29, 2021): 705. http://dx.doi.org/10.3390/min11070705.

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Laser-induced breakdown spectroscopy (LIBS) is a simple and straightforward technique of atomic emission spectroscopy that can provide multi-element detection and quantification in any material, in-situ and in real time because all elements emit in the 200–900 nm spectral range of the LIBS optical emission. This study evaluated two practical applications of LIBS—validation of labels assigned to garnets in museum collections and discrimination of LCT (lithium-cesium-tantalum) and NYF (niobium, yttrium and fluorine) pegmatites based on garnet geochemical fingerprinting, both of which could be implemented on site in a museum or field setting with a handheld LIBS analyzer. Major element compositions were determined using electron microprobe analysis for a suite of 208 garnets from 24 countries to determine garnet type. Both commercial laboratory and handheld analyzers were then used to acquire LIBS broadband spectra that were chemometrically processed by partial least squares discriminant analysis (PLSDA) and linear support vector machine classification (SVM). High attribution success rates (>98%) were obtained using PLSDA and SVM for the handheld data suggesting that LIBS could be used in a museum setting to assign garnet type quickly and accurately. LIBS also identifies changes in garnet composition associated with increasing mineral and chemical complexity of LCT and NYF pegmatites.

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Traynor, Damien, Shiyamala Duraipandian, Ramya Bhatia, Kate Cuschieri, Prerna Tewari, Padraig Kearney, Tom D’Arcy, John J. O’Leary, Cara M. Martin, and Fiona M. Lyng. "Development and Validation of a Raman Spectroscopic Classification Model for Cervical Intraepithelial Neoplasia (CIN)." Cancers 14, no. 7 (April 6, 2022): 1836. http://dx.doi.org/10.3390/cancers14071836.

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The mortality associated with cervical cancer can be reduced if detected at the precancer stage, but current methods are limited in terms of subjectivity, cost and time. Optical spectroscopic methods such as Raman spectroscopy can provide a rapid, label-free and nondestructive measurement of the biochemical fingerprint of a cell, tissue or biofluid. Previous studies have shown the potential of Raman spectroscopy for cervical cancer diagnosis, but most were pilot studies with small sample sizes. The aim of this study is to show the clinical utility of Raman spectroscopy for identifying cervical precancer in a large sample set with validation in an independent test set. Liquid-based cervical cytology samples (n = 662) (326 negative, 200 cervical intraepithelial neoplasia (CIN)1 and 136 CIN2+) were obtained as a training set. Raman spectra were recorded from single-cell nuclei and subjected to a partial least squares discriminant analysis (PLSDA). In addition, the PLSDA classification model was validated using a blinded independent test set (n = 69). A classification accuracy of 91.3% was achieved with only six of the blinded samples misclassified. This study showed the potential clinical utility of Raman spectroscopy with a good classification of negative, CIN1 and CIN2+ achieved in an independent test set.

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Traynor, Damien, Cara M. Martin, Christine White, Stephen Reynolds, Tom D’Arcy, John J. O’Leary, and Fiona M. Lyng. "Raman Spectroscopy of Liquid-Based Cervical Smear Samples as a Triage to Stratify Women Who Are HPV-Positive on Screening." Cancers 13, no. 9 (April 22, 2021): 2008. http://dx.doi.org/10.3390/cancers13092008.

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The role of persistent high-risk human papillomavirus (HPV) infection in the development of cervical precancer and cancer is now well accepted, and HPV testing has recently been introduced for primary cervical screening. However, the low specificity of HPV DNA testing can result in large numbers of women with an HPV-positive result, and additional triage approaches are needed to avoid over-referral to colposcopy and overtreatment. The aim of this study was to assess Raman spectroscopy as a potential triage test to discriminate between transient and persistent HPV infection. HPV DNA status and mRNA status were confirmed in ThinPrep® cervical samples (n = 60) using the Cobas 4800 and APTIMA HPV test, respectively. Raman spectra were recorded from single-cell nuclei and subjected to partial least squares discriminant analysis (PLSDA). In addition, the PLSDA classification model was validated using a blinded independent test set (n = 14). Sensitivity of 85% and specificity of 92% were achieved for the classification of transient and persistent HPV infection, and this increased to 90% sensitivity and 100% specificity when mean sample spectra were used instead of individual cellular spectra. This study showed that Raman spectroscopy has potential as a triage test for HPV-positive women to identify persistent HPV infection.

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Hark, Richard R., Chandra S. Throckmorton, Russell S. Harmon, John R. Plumer, Karen A. Harmon, J. Bruce Harrison, Jan M. H. Hendrickx, and Jay L. Clausen. "Multianalyzer Spectroscopic Data Fusion for Soil Characterization." Applied Sciences 10, no. 23 (December 5, 2020): 8723. http://dx.doi.org/10.3390/app10238723.

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The ability to rapidly conduct in-situ chemical analysis of multiple samples of soil and other geological materials in the field offers many advantages over a traditional approach that involves collecting samples for subsequent examination in the laboratory. This study explores the application of complementary spectroscopic analyzers and a data fusion methodology for the classification/discrimination of >100 soil samples from sites across the United States. Commercially available, handheld analyzers for X-ray fluorescence spectroscopy (XRFS), Raman spectroscopy (RS), and laser-induced breakdown spectroscopy (LIBS) were used to collect data both in the laboratory and in the field. Following a common data pre-processing protocol, principal component analysis (PCA) and partial least squares discriminant analysis (PLSDA) were used to build classification models. The features generated by PLSDA were then used in a hierarchical classification approach to assess the relative advantage of information fusion, which increased classification accuracy over any of the individual sensors from 80-91% to 94% and 64-93% to 98% for the two largest sample suites. The results show that additional testing with data sets for which classification with individual analyzers is modest might provide greater insight into the limits of data fusion for improving classification accuracy.

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Bai, Xiuyun, Hengye Chen, Wanjun Long, Wei Lan, Siyu Wang, Guanghua Lei, Yuting Guan, Jian Yang, and Haiyan Fu. "Accurate Traceability of Stable C, H, O, N Isotope Ratios and Multi-Element Analysis Combined with Chemometrics for Chrysanthemi Flos ‘Hangbaiju’ from Different Origins." Chemosensors 10, no. 12 (December 12, 2022): 529. http://dx.doi.org/10.3390/chemosensors10120529.

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Chrysanthemi Flos ‘Hangbaiju’ (HBJ) is a common Chinese medicinal material with the same origin as the medicinal and edible cognate plant in China, whose quality is seriously affected by the place of origin. In this study, four stable isotope ratios (δ15N, δ2H, δ13C, and δ18O) and 44 elements were detected and analyzed in 191 HBJ flower samples from six locations in China to trace the origin of HBJ. An ANOVA analysis of δ15N, δ2H, δ13C, and δ18O values, as well as milti-elements, showed that there were significant differences among the six places of origin. Partial least squares discriminant analysis (PLSDA) and one-class partial least squares discriminant analysis (OPLS-DA) models were established to trace the origin of HBJ from these six locations. The results showed that the classification effect of the PLSDA model is poor; however, the established OPLS-DA model can distinguish between products of national geographic origin (Tongxiang City, Zhejiang Province, China) and samples from other origins, among which Ni, Mo, δ13C, Cu, and Ce elements (VIP > 1) contribute the most to this classification. Therefore, this study provides a new method for tracing the origins of HBJ, which is of great significance for the protection of origin labeling of products.

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Rui, Wen, Hong Yuan Chen, Yi Fan Feng, Zhong Feng Shi, and Miao Miao Jiang. "Comparision of Bupleurum scorzoneri folium Willd. Grouping from Different Habitats Based on Pattern Recognition with R Software." Advanced Materials Research 393-395 (November 2011): 1139–42. http://dx.doi.org/10.4028/www.scientific.net/amr.393-395.1139.

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Bupleurum scorzoneri folium Willd.(BSFW) is a traditional Chinese medicine which is widely distributed in China. To evaluate the quality of BSFW from different habitats, samples from 5 different areas in China were determined by UPLC/MS. The chemical data were dealed with hierarchical clustering, PCA, SPCA, PLSDA and SPLSDA using R software. The results show that these pattern recognition methods can fully reflect the chemical composition of different areas of BSFW, which make it possible to control the quality.

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Tang, Bang-Cheng, Hai-Yan Fu, Qiao-Bo Yin, Zeng-Yan Zhou, Wei Shi, Lu Xu, and Yuan-Bin She. "Combining Near-Infrared Spectroscopy and Chemometrics for Rapid Recognition of an Hg-Contaminated Plant." Journal of Spectroscopy 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/3597451.

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The feasibility of rapid recognition of an Hg-contaminated plant as a soil pollution indicator was investigated using near-infrared spectroscopy (NIRS) and chemometrics. The stem and leave of a native plant,Miscanthus floridulus(Labill.) Warb. (MFLW), were collected from Hg-contaminated areas (n1=125) as well as from regular areas (n2=116). The samples were dried and crushed and the powders were sieved through an 80-mesh sieve. Reference analysis of Hg levels was performed using inductively coupled plasma-atomic emission spectrometry (ICP-AES). The actual Hg contents of contaminated and normal samples were 16.2–30.5 and 0.0–0.1 mg/Kg, respectively. The NIRS measurements of impacted sample powders were collected in the mode of reflectance. The DUPLEX algorithm was utilized to split the NIRS data into representative training and test sets. Different spectral preprocessing methods were performed to remove the unwanted and noncomposition-correlated spectral variations. Classification models were developed using partial least squares discrimination analysis (PLSDA) based on the raw, smoothed, second-order derivative (D2), and standard normal variate (SNV) data, respectively. The prediction accuracy obtained by PLSDA with each data preprocessing option was 100%, indicating pattern recognition of Hg-contaminated MFLW samples using NIRS data was in perfect consistence with the ICP-AES results. NIRS combined with chemometrics will provide a tool to screen the Hg-contaminated MFLW, which can be potentially used as an indicator of soil pollution.

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Jiao, Yun, Alan Faden, Boris Sabirzhanov, Bogdan Stoica, Gregory L. Szeto, and Jennie Leach. "Transcriptional profiling predicts behavioral performance in experimental mouse model of traumatic brain injury (TBI)." Journal of Immunology 204, no. 1_Supplement (May 1, 2020): 75.17. http://dx.doi.org/10.4049/jimmunol.204.supp.75.17.

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Abstract Traumatic brain injury (TBI) is a prevailing cause of disability and death. There are no curative therapies or robust predictors of long-term neurodegenerative disease. Treatment is challenging due to poor understanding of the cellular responses and mechanisms underlying TBI. We linked transcriptional profiling, cytokine profiling, and behavioral scoring in experimental TBI to discover new insights into immune-mediated mechanisms of TBI. Mice received ipsilateral moderate controlled cortical impaction. Luminex analyses showed upregulation of inflammatory cytokines in injured cortex, hippocampus, and spinal cord. Neurobehavioral evaluations were performed, then hippocampi isolated at 28d post-TBI for RNAseq. Pathway analyses of upregulated genes were functionally enriched for glial activation, proliferation, and neuronal development. Genes involved in synaptic transmission, glutamate receptor signaling, and neuronal migration were downregulated. Partial least squares (PLS) regression successfully modeled the relationship between differentially expressed genes and behavioral scores (R2=0.94). A core 10-gene signature predicted TBI severity and behavioral score. PLS discriminant analysis (PLSDA) classified TBI severity. VIP scores were used to define a 20-gene signature in TBI based on high VIP score, high fold change, and contribution to biological function. A PLSDA model using only these 20 genes was cross-validated by bootstrap method (AUROC=0.93), indicating that these genes may identify the critical TBI-altered processes. Altogether, our analyses revealed TBI-related biological pathways, potential therapeutic targets, and gene signatures as biomarkers for TBI severity and behavioral outcomes.

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Sedjoah, Rita-Cindy Aye-Ayire, Bangxing Han, and Hui Yan. "Identification of geographical origin of TPFD based on handheld NIR spectroscopy and PLSDA." NIR news 31, no. 5-6 (August 6, 2020): 25–29. http://dx.doi.org/10.1177/0960336020944007.

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The present study is focused on the identification of geographical origin (Zhejiang, Yunnan and Anhui, China) of Dendrobium officinale’s dried stem called Tiepi fengdou by mean of the handheld near-infrared spectrometer. Raw data were preprocessed to reduce unwanted spectral variations by the first-order derivative followed by standard normal variate transformation, and partial least squares discriminant analysis model was developed for calibration. The results showed that more than 90% of the origins were identified. Therefore, it is possible to classify the geographical origin of Tiepi fengdou by the use of the handheld near-infrared spectrometer for effective quality control.

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Avram, Sorin, Liliana M. Pacureanu, Edward Seclaman, Alina Bora, and Ludovic Kurunczi. "PLS-DA - Docking Optimized Combined Energetic Terms (PLSDA-DOCET) Protocol: A Brief Evaluation." Journal of Chemical Information and Modeling 51, no. 12 (December 7, 2011): 3169–79. http://dx.doi.org/10.1021/ci2002268.

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Holandino, Carla, Michelle Nonato de Oliveira Melo, Adriana Passos Oliveira, Rafael Garret, Mirio Grazi, Hartmutt Ramm, Tim Jaeger, and Stephan Baumgartner. "Metabolomic analysis of Viscum album L homeopathic tinctures and antitumor studies in 3D spheroid models." International Journal of High Dilution Research - ISSN 1982-6206 18, no. 02 (June 30, 2021): 18. http://dx.doi.org/10.51910/ijhdr.v18i02.996.

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Background The antitumoral efficacy of aqueous Viscum album extracts is attributed to the presence of lectins and viscotoxins. However, previous studies demonstrated an antitumoral activity of European V. album ethanolic homeopathic tinctures (VAHT) prepared according to homeopathic methodology. Aims To investigate the seasonal influences (summer and winter) in the metabolomic profile of V. album ssp. homeopathic mother tinctures (VAHT) and to evaluate the antitumoral activity of some VAHT in 3D tumor spheroid models. Methodology The following VAHT were prepared by ethanolic maceration: V. album ssp. album growing on Malus domestica, Quercus sp. and Ulmus sp.; V. album ssp. austriacum from Pinus sylvestris; V. album ssp. abietis from Abies alba. Chemical analyses were performed using liquid chromatography coupled to high-resolution mass spectrometry. Data was submitted to multivariate statistical analysis using principal component analysis (PCA) and Partial Least Squares Discriminant Analysis (PLSDA) in Metaboanalyst platform. The antitumor potential of VAHT (0.5% v/v) was conducted in 3D tumor spheroid models (MDA-MB-231 cell line) by MTT for 72 hours. Results and discussion The PCA analysis explained 40% of data variation and clustered VAHT samples into 3 groups, emphasizing the chemical similarity between the botanical subspecies of V. album. Some key compounds were mainly responsible for this separation: pinobankasin hexose-pentose (V. album ssp. abietis); citric acid (V. album ssp. austriacum); malic acid (V. album ssp. album). The chemical differences among summer and winter samples, detected by PLSDA, were related to the Viscum album host trees. A significant reduction of 50% and 41% (p

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Rittiron, Ronnarit, Sureeporn Narongwongwattana, Unaruj Boonprakob, and Worapa Seehalak. "Rapid and nondestructive detection of watercore and sugar content in Asian pear by near infrared spectroscopy for commercial trade." Journal of Innovative Optical Health Sciences 07, no. 06 (October 21, 2014): 1350073. http://dx.doi.org/10.1142/s1793545813500739.

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Watercore and sugar content are internal qualities which are impossible for exterior determination. Therefore the aims of this study were to develop models for nondestructive detection of watercore and predicting sugar content in pear using Near Infrared Spectroscopy (NIR) technique. A total of 93 samples of Asian pear variety "SH-078" were used. For sugar content, spectrum of each fruit was measured in the short wavelength region (700–1100 nm) in the reflection mode and the first derivative of spectra were then correlated with the sugar content in juice determined by digital refractometer. Prediction equation was performed by multiple linear regression. The result showed Standard Error of Prediction (SEP) = 0.58°Bx, and Bias=0.11. The result from t-test showed that sugar content predicted by NIR was not significantly different from the value analyzed by refractometer at 95% confidence. For watercore disorder, NIR measurement was performed over the short wavelength range (700–850 nm) in the transmission mode. The first derivative spectra were correlated with internal qualities. Then principle component analysis (PCA) and partial least squares discriminant analysis (PLSDA) were used to perform discrimination models. The accuracy of the PCA model was greater than the PLSDA one. The scores from PC1 were separated into two boundaries, one predicted rejected pears with 100% classification accuracy, and the other was accepted pears with 92% accuracy. The high accuracy of sugar content determining and watercore detecting by NIR reveal the high efficiency of NIR technique for detecting other internal qualities of fruit in the future.

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Lu, Yi, Changbao Yang, and Zhiguo Meng. "Lithology Discrimination Using Sentinel-1 Dual-Pol Data and SRTM Data." Remote Sensing 13, no. 7 (March 27, 2021): 1280. http://dx.doi.org/10.3390/rs13071280.

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Compared to various optical remote sensing data, studies on the performance of dual-pol Synthetic aperture radar (SAR) on lithology discrimination are scarce. This study aimed at using Sentinel-1 data to distinguish dolomite, andesite, limestone, sandstone, and granite rock types. The backscatter coefficients VV and VH, the ratio VV–VH; the decomposition parameters Entropy, Anisotropy, and Alpha were firstly derived and the Kruskal–Wallis rank sum test was then applied to these polarimetric derived matrices to assess the significance of statistical differences among different rocks. Further, the corresponding gray-level co-occurrence matrices (GLCM) features were calculated. To reduce the redundancy and data dimension, the principal component analysis (PCA) was carried out on the GLCM features. Due to the limited rock samples, before the lithology discrimination, the input variables were selected. Several classifiers were then used for lithology discrimination. The discrimination models were evaluated by overall accuracy, confusion matrices, and the area under the curve-receiver operating characteristics (AUC-ROC). Results show that (1) the statistical differences of the polarimetric derived matrices (backscatter coefficients, ratio, and decomposition parameters) among different rocks was insignificant; (2) texture information derived from Sentinel-1 had great potential for lithology discrimination; (3) partial least square discrimination analysis (PLSDA) had the highest overall accuracy (0.444) among the classification models; (4) though the overall accuracy is unsatisfactory, according to the AUC-ROC and confusion matrices, the predictive ability of PLSDA model for limestone is high with an AUC value of 0.8017, followed by dolomite with an AUC value of 0.7204. From the results, we suggest that the dual-pol Sentinel-1 data are able to correctly distinguish specific rocks and has the potential to capture the variation of different rocks.

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Aeberli, Aaron, Andrew Robson, Stuart Phinn, David W. Lamb, and Kasper Johansen. "A Comparison of Analytical Approaches for the Spectral Discrimination and Characterisation of Mite Infestations on Banana Plants." Remote Sensing 14, no. 21 (October 30, 2022): 5467. http://dx.doi.org/10.3390/rs14215467.

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This research investigates the capability of field-based spectroscopy (350–2500 nm) for discriminating banana plants (Cavendish subgroup Williams) infested with spider mites from those unaffected. Spider mites are considered a major threat to agricultural production, as they occur on over 1000 plant species, including banana plant varieties. Plants were grown under a controlled glasshouse environment to remove any influence other than the imposed treatment (presence or absence of spider mites). The spectroradiometer measurements were undertaken with a leaf clip over three infestation events. From the resultant spectral data, various classification models were evaluated including partial least squares discriminant analysis (PLSDA), K-nearest neighbour, support vector machines and back propagation neural network. Wavelengths found to have a significant response to the presence of spider mites were extracted using competitive adaptive reweighted sampling (CARS), sub-window permutation analysis (SPA) and random frog (RF) and benchmarked using the classification models. CARS and SPA provided high detection success (86% prediction accuracy), with the wavelengths found to be significant corresponding with the red edge and near-infrared portions of the spectrum. As there is limited access to operational commercial hyperspectral imaging and additional complexity, a multispectral camera (Sequoia) was assessed for detecting spider mite impacts on banana plants. Simulated multispectral bands were able to provide a high level of detection accuracy (prediction accuracy of 82%) based on a PLSDA model, with the near-infrared band being most important, followed by the red edge, green and red bands. Multispectral vegetation indices were trialled using a simple threshold-based classification method using the green normalised difference vegetation index (GNDVI), which achieved 82% accuracy. This investigation determined that remote sensing approaches can provide an accurate method of detecting mite infestations, with multispectral sensors having the potential to provide a more commercially accessible means of detecting outbreaks.

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Ryckewaert, Maxime, Maxime Metz, Daphné Héran, Pierre George, Bruno Grèzes-Besset, Reza Akbarinia, Jean-Michel Roger, and Ryad Bendoula. "Massive spectral data analysis for plant breeding using parSketch-PLSDA method: Discrimination of sunflower genotypes." Biosystems Engineering 210 (October 2021): 69–77. http://dx.doi.org/10.1016/j.biosystemseng.2021.08.005.

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46

Fuller, Harriett, J. Bernadette Moore, Mark Iles, and Michael Zulyniak. "Distinct Serum Metabolic Profiles in Early Pregnancy Characterise Gestational Diabetes Mellitus Incidence in High-Risk Women." Current Developments in Nutrition 5, Supplement_2 (June 2021): 748. http://dx.doi.org/10.1093/cdn/nzab046_045.

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Abstract Objectives This study aimed to investigate and characterise the metabolic profiles of gestational diabetes mellitus (GDM) in white European (WE) and British Pakistani (BP) women with and without GDM. Methods 146 serum metabolites quantified by nuclear magnetic resonance, from 2668 WEs and 2671 BP pregnant women ≤28 weeks’ gestation from the Born in Bradford cohort were analysed. Partial least squares discriminatory analyses (PLSDA) and sparse PLSDA (sPLSDA) were used to investigate the ethnic-specific metabolite signatures of future incidence of GDM. Metabolite features driven by pre-pregnancy weight status and known GDM risk factors (age, parity, multiple pregnancies and smoking status) were also explored. Results Models explained 60% of variance between future GDM cases/non-cases in WE mothers but only 35% in BPs, after adjusting for BMI, age, parity, smoking, and non-singleton pregnancies. Across both ethnic groups, 8 metabolites associated with future GDM status. When ethnic groups were investigated independently, 7 additional metabolites associated with future GDM in WEs were identified, while no additional associations were observed in BPs. Further stratification by BMI (healthy vs overweight/obese) uncovered a metabolic profile (characterised by fatty acids and glycolytic metabolites) unique to normal-weight BP women who later developed GDM. No difference was observed between the metabolic profiles of overweight/obese women, irrespective of ethnicity. Conclusions In early pregnancy, the metabolomes of future GDM cases and non-cases are distinct and differ by ethnicity. While the metabolic profiles of overweight women are largely similar in WE and BPs, a unique metabolic profile was observed in healthy weight BP women who went on to develop GDM and offers insight into the early metabolomic perturbations that precede GDM development in this high-risk (but healthy) population. Further exploration regarding the interaction between genetics and lifestyle on modifying metabolic profiles associated with GDM risk will help inform ethnically appropriate nutrition strategies. Funding Sources HF is supported by a doctoral scholarship from the University of Leeds.

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Wagala, Adolphus, Graciela González-Farías, Rogelio Ramos та Oscar Dalmau. "PLS Generalized Linear Regression and Kernel Multilogit Algorithm (KMA) for Microarray Data Classiﬁcation Problem". Revista Colombiana de Estadística 43, № 2 (1 липня 2020): 233–49. http://dx.doi.org/10.15446/rce.v43n2.81811.

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This study involves the implentation of the extensions of the partial least squares generalized linear regression (PLSGLR) by combining it with logistic regression and linear discriminant analysis, to get a partial least squares generalized linear regression-logistic regression model (PLSGLR-log), and a partial least squares generalized linear regression-linear discriminant analysis model (PLSGLRDA). A comparative study of the obtained classiﬁers with the classical methodologies like the k-nearest neighbours (KNN), linear discriminant analysis (LDA), partial least squares discriminant analysis (PLSDA), ridge partial least squares (RPLS), and support vector machines(SVM) is then carried out. Furthermore, a new methodology known as kernel multilogit algorithm (KMA) is also implemented and its performance compared with those of the other classiﬁers. The KMA emerged as the best classiﬁer based on the lowest classiﬁcation error rates compared to the others when applied to the types of data are considered; the un- preprocessed and preprocessed.

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Yan, Si-Min, Jun-Ping Liu, Lu Xu, Xian-Shu Fu, Hai-Feng Cui, Zhen-Yu Yun, Xiao-Ping Yu, and Zi-Hong Ye. "Rapid Discrimination of the Geographical Origins of an Oolong Tea (Anxi-Tieguanyin) by Near-Infrared Spectroscopy and Partial Least Squares Discriminant Analysis." Journal of Analytical Methods in Chemistry 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/704971.

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This paper focuses on a rapid and nondestructive way to discriminate the geographical origin of Anxi-Tieguanyin tea by near-infrared (NIR) spectroscopy and chemometrics. 450 representative samples were collected from Anxi County, the original producing area of Tieguanyin tea, and another 120 Tieguanyin samples with similar appearance were collected from unprotected producing areas in China. All these samples were measured by NIR. The Stahel-Donoho estimates (SDE) outlyingness diagnosis was used to remove the outliers. Partial least squares discriminant analysis (PLSDA) was performed to develop a classification model and predict the authenticity of unknown objects. To improve the sensitivity and specificity of classification, the raw data was preprocessed to reduce unwanted spectral variations by standard normal variate (SNV) transformation, taking second-order derivatives (D2) spectra, and smoothing. As the best model, the sensitivity and specificity reached 0.931 and 1.000 with SNV spectra. Combination of NIR spectrometry and statistical model selection can provide an effective and rapid method to discriminate the geographical producing area of Anxi-Tieguanyin.

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LIU, C., W. LIU, X. LU, W. CHEN, F. CHEN, J. YANG, and L. ZHENG. "Non-destructive discrimination of conventional and glyphosate-resistant soybean seeds and their hybrid descendants using multispectral imaging and chemometric methods." Journal of Agricultural Science 154, no. 1 (November 10, 2014): 1–12. http://dx.doi.org/10.1017/s0021859614001142.

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SUMMARYSoybean is an important oil- and protein-producing crop and over the last few decades soybean genetic transformation has made rapid strides. The probability of occurrence of transgene flow should be assessed, although the discrimination of conventional and transgenic soybean seeds and their hybrid descendants is difficult in fields. The feasibility of non-destructive discrimination of conventional and glyphosate-resistant soybean seeds and their hybrid descendants was examined by a multispectral imaging system combined with chemometric methods. Principal component analysis (PCA), partial least squares discriminant analysis (PLSDA), least squares-support vector machines (LS-SVM) and back propagation neural network (BPNN) methods were applied to classify soybean seeds. The current results demonstrated that clear differences among conventional and glyphosate-resistant soybean seeds and their hybrid descendants could be easily visualized and an excellent classification (98% with BPNN model) could be achieved. It was concluded that multispectral imaging together with chemometric methods would be a promising technique to identify transgenic soybean seeds with high efficiency.

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Cui, Hai-Feng, Zi-Hong Ye, Lu Xu, Xian-Shu Fu, Cui-Wen Fan, and Xiao-Ping Yu. "Automatic and Rapid Discrimination of Cotton Genotypes by Near Infrared Spectroscopy and Chemometrics." Journal of Analytical Methods in Chemistry 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/793468.

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This paper reports the application of near infrared (NIR) spectroscopy and pattern recognition methods to rapid and automatic discrimination of the genotypes (parent, transgenic, and parent-transgenic hybrid) of cotton plants. Diffuse reflectance NIR spectra of representative cotton seeds (n=120) and leaves (n=123) were measured in the range of 4000–12000 cm−1. A practical problem when developing classification models is the degradation and even breakdown of models caused by outliers. Considering the high-dimensional nature and uncertainty of potential spectral outliers, robust principal component analysis (rPCA) was applied to each separate sample group to detect and exclude outliers. The influence of different data preprocessing methods on model prediction performance was also investigated. The results demonstrate that rPCA can effectively detect outliers and maintain the efficiency of discriminant analysis. Moreover, the classification accuracy can be significantly improved by second-order derivative and standard normal variate (SNV). The best partial least squares discriminant analysis (PLSDA) models obtained total classification accuracy of 100% and 97.6% for seeds and leaves, respectively.

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