Relevant bibliographies by topics / Modified partial least squares regression (MPLS)

Academic literature on the topic 'Modified partial least squares regression (MPLS)'

Author: Grafiati
Published: 10 December 2022
Last updated: 19 February 2023

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Journal articles on the topic "Modified partial least squares regression (MPLS)"

1

Guthrie, J. A., C. J. Liebenberg, and K. B. Walsh. "NIR model development and robustness in prediction of melon fruit total soluble solids." Australian Journal of Agricultural Research 57, no. 4 (2006): 411. http://dx.doi.org/10.1071/ar05123.

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Near infrared spectroscopy (NIRS) can be used for the on-line, non-invasive assessment of fruit for eating quality attributes such as total soluble solids (TSS). The robustness of multivariate calibration models, based on NIRS in a partial transmittance optical geometry, for the assessment of TSS of intact rockmelons (Cucumis melo) was assessed. The mesocarp TSS was highest around the fruit equator and increased towards the seed cavity. Inner mesocarp TSS levels decreased towards both the proximal and distal ends of the fruit, but more so towards the proximal end. The equatorial region of the fruit was chosen as representative of the fruit for near infrared assessment of TSS. The spectral window for model development was optimised at 695–1045 nm, and the data pre-treatment procedure was optimised to second-derivative absorbance without scatter correction. The ‘global’ modified partial least squares (MPLS) regression modelling procedure of WINISI (ver. 1.04) was found to be superior with respect to root mean squared error of prediction (RMSEP) and bias for model predictions of TSS across seasons, compared with the ‘local’ MPLS regression procedure. Updating of the model with samples selected randomly from the independent validation population demonstrated improvement in both RMSEP and bias with addition of approximately 15 samples.
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2

Cozzolino, D., I. Murray, R. Paterson, and J. R. Scaife. "Visible and near Infrared Reflectance Spectroscopy for the Determination of Moisture, Fat and Protein in Chicken Breast and Thigh Muscle." Journal of Near Infrared Spectroscopy 4, no. 1 (January 1996): 213–23. http://dx.doi.org/10.1255/jnirs.92.

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Near infrared (NIR) reflectance spectroscopy was used to determine the chemical composition of chicken breast and thigh muscles. Samples from twenty-four males and twenty-four females were scanned from 400 to 2500 nm, both as intact muscle and as comminuted (minced) tissue. Modified partial least squares (MPLS) regression on scatter corrected spectra (standard normal variates and Detrend) gave calibration models for chemical variables from NIR measurements on the defrosted minced breast samples having multivariate correlation coefficients and standard errors of calibration of 0.995 (2.4), 0.974 (2.11) and 0.946 (4.55) for moisture, crude protein and fat in g kg −1, respectively.
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3

Vranić, Marina, Marko Petek, Krešimir Bošnjak, Boris Lazarević, and Klaudija Carović Stanko. "Prediction of macro- and microelements content in Croatian common bean landraces (Phaseolus vulgaris L.) by NIR spectroscopy." Poljoprivreda 25, no. 1 (June 10, 2019): 48–55. http://dx.doi.org/10.18047/poljo.25.1.7.

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In this study, near-infrared spectroscopy (NIRS) was used to predict the contents of essential macro- and microelements in common bean (Phaseolus vulgaris L.) accessions of most widespread Croatian landraces. Total of 175 samples were used for the model development by modified partial least square (MPLS), principal component regression (PCR) and partial least square (PLS) techniques. Based on the coefficients of determination (R2), standard error of calibration (SEC) and error of prediction (SEP) the models developed were (i) nearly applicable for nitrogen (N) (0.89, 0.12 and 0.45 respectively), (ii) poor for iron (Fe), cinc (Zn), potassium oxide (K2O) and potassium (K), (iii) usable for phosphorus pentoxide (P2O5), phosphorus (P), phytic acid (PA) and manganese (Mn). The MPLS regression statistics suggested the most accurate models developed comparing with PLS and PCR. It was concluded that a wider set of common bean samples needs to be used for macro- and microelements prediction by NIRS.
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4

Henry Buregyeya, Steven Kashub. Tumwesigye, Ephraim Nuwamanya, Moses Matovu, Priver Namanya, Kephas Nowankunda, Wilberforce K Tushemereirwe, and Patrick Rubaihayo. "A high throughput phenotyping technique for banana cultivar Sukali Ndizi based on internal fruit quality attributes." International Journal of Science and Technology Research Archive 3, no. 2 (December 30, 2022): 258–69. http://dx.doi.org/10.53771/ijstra.2022.3.2.0155.

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Background: Sukali Ndizi quality traits such as Total soluble solid (TSS) content, pulp texture and sugar/acid (S/A) ratio are critical in quality assessment. Screening very large numbers of fruit genotypes has prompted the development of a high throughput method using Near Infrared spectrometry (NIRS). Results: The calibration procedure for the attributes of TSS, pulp texture and S/A ratio was optimized with respect to a reference sampling technique, scan averaging, spectral window, data pre-treatment and regression procedure. Calibration equations for all analytical characteristics were computed by NIR Software ISI Present WINISI using Modified Partial Least Squares (MPLS) and Partial Least Squares. The quality of calibration models were evaluated by Standard Error of Calibration and coefficient of determination parameters between the measured and the predicted values. The results obtained with FOSS NIR systems 2500 spectrometer (model DS 2500) using the 350-2500 nm range, showed good prediction of the quality traits TSS content, pulp texture and S/A ratio. The MPLS method produced satisfactory Calibration model performance for TSS, texture and S/A ratio, with typical Rc2 of 0.73%Brix, 0.69kgf and 0.7; and root mean squared standard error of calibration of 0.73%Brix, 0.25kgf and 5.36 respectively. This is a good set of quality traits predicting Sukali Ndizi quality with NIRS with robustness, as it was obtained by using diverse Ndizi populations. Conclusions: This can be a useful tool to phenotype large numbers of Ndizi hybrids per day, making it possible to reduce on the resources spent when utilizing organoleptic evaluation selection technique.
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5

Toledo-Martín, Eva, María García-García, Rafael Font, José Moreno-Rojas, María Salinas-Navarro, Pedro Gómez, and Mercedes del Río-Celestino. "Quantification of Total Phenolic and Carotenoid Content in Blackberries (Rubus Fructicosus L.) Using Near Infrared Spectroscopy (NIRS) and Multivariate Analysis." Molecules 23, no. 12 (December 4, 2018): 3191. http://dx.doi.org/10.3390/molecules23123191.

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A rapid method to quantify the total phenolic content (TPC) and total carotenoid content (TCC) in blackberries using near infrared spectroscopy (NIRS) was carried out aiming to provide reductions in analysis time and cost for the food industry. A total of 106 samples were analysed using the Folin-Ciocalteu method for TPC and a method based on Ultraviolet-Visible Spectrometer for TCC. The average contents found for TPC and TCC were 24.27 mg·g−1 dw and 8.30 µg·g−1 dw, respectively. Modified partial least squares (MPLS) regression was used for obtaining the calibration models of these compounds. The RPD (ratio of the standard deviation of the reference data to the standard error of prediction (SEP)) values from external validation for both TPC and TCC were between 1.5 < RPDp < 2.5 and RER values (ratio of the range in the reference data to SEP) were 5.92 for TPC and 8.63 for TCC. These values showed that both equations were suitable for screening purposes. MPLS loading plots showed a high contribution of sugars, chlorophyll, lipids and cellulose in the modelling of prediction equations.
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6

Goodchild, A. V., F. J. El Haramein, A. Abd El Moneim, H. P. S. Makkar, and P. C. Williams. "Prediction of Phenolics and Tannins in Forage Legumes by near Infrared Reflectance." Journal of Near Infrared Spectroscopy 6, no. 1 (January 1998): 175–81. http://dx.doi.org/10.1255/jnirs.134.

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Near infrared (NIR) spectroscopy calibrations for measures of tannins and nutritive value were made on a set of 40 hays and straws of Vicia and Lathyrus spp. by the modified partial least squares (MPLS) method and were evaluated by cross-validation. They successfully predicted, in the dry matter, 4.6–34.1 g kg−1 total phenolics with a cross-validation R2 of 0.95 and a SECV of 1.68 g kg−1, 1.3–23.1 g kg−1 total tannins ( R2 = 0.89, SECV = 1.84 g kg−1) and 0.5–30.3 g kg−1 condensed tannins ( R2 = 0.93, SECV = 2.34 g kg−1). In multiple regression and MPLS calibrations, a wavelength close to 2.144 μm was common to all measures of tannins, and was attributed to condensed tannins and its flavanoid precursors. The biological activity of tannins on rumen microbes, measured as a 0–6.9% effect on gas production with rumen liquor in vitro, was less precisely predicted by MPLS ( R2 = 0.49, SECV = 1.49%). The biological activity per gram of chemical tannins could not be predicted by NIR spectroscopy in the material studied. Acid detergent fibre, neutral detergent fibre, crude protein and gas production in vitro were predicted with R2 = 0.95 to 0.96 ( SECV = 18.2, 24.8, 10.1 g kg−1 or 7.2 mL g−1).
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7

Cozzolino, D., I. Murray, J. R. Scaife, and R. Paterson. "Study of dissected lamb muscles by visible and near infrared reflectance spectroscopy for composition assessment." Animal Science 70, no. 3 (June 2000): 417–23. http://dx.doi.org/10.1017/s1357729800051766.

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AbstractNear infrared reflectance spectroscopy (NIRS) was used to study the reflectance properties of intact and minced lamb muscles in two presentations to the instrument to predict their chemical composition. A total of 306 muscles were examined from 51 lambs, consisting of the following muscles: longissimus dorsi, supraspinatus, infraspinatus, semimembranosus, semitendinosus and rectus femoris. Modified partial least squares (MPLS) regression models of chemical variables yielded R2 and standard error of cross-validation (SECV) of 0·76 (SECV: 10·4), 0·83 (SECV: 5·5) and 0·73 (SECV: 4·7) for moisture, crude protein and intramuscular fat in the minced samples expressed as g/kg on a fresh-weight basis, respectively. Calibrations for intact samples had lower R2 and higher standard error of cross validation (SECV) compared with the minced samples.
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8

Hallett, R. A., J. W. Hornbeck, and M. E. Martin. "Predicting Elements in White Pine and Red Oak Foliage with Visible-Near Infrared Reflectance Spectroscopy." Journal of Near Infrared Spectroscopy 5, no. 2 (March 1997): 77–82. http://dx.doi.org/10.1255/jnirs.101.

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Near infrared (NIR) reflectance spectroscopy was evaluated for its effectiveness at predicting Al, Ca, Fe, K, Mg and Mn concentrations in white pine ( Pinus strobus L.) and red oak ( Quercus rubra L.) foliage. A NIR spectrophotometer was used to scan 470 dried, ground foliage samples. These samples were used to develop calibration equations using a modified partial least squares (MPLS) regression technique. For the calibration equations, concentrations of Al, Ca, Fe, K, Mg and Mn as determined by acid digestion and laboratory analysis were regressed against second-difference absorbance values measured from 400 to 2498 nm. The regression models developed by NIR reflectance spectroscopy were unable to predict Fe. Predictions were satisfactory for Al, Ca, K, Mn and Mg. It still is uncertain which mineral/organic associations are being detected by NIR reflectance spectroscopy. Future applications may include prediction of element concentrations in the forest canopy via remote sensing.
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9

Confalonieri, M., F. Fornasier, A. Ursino, F. Boccardi, B. Pintus, and M. Odoardi. "The Potential of near Infrared Reflectance Spectroscopy as a Tool for the Chemical Characterisation of Agricultural Soils." Journal of Near Infrared Spectroscopy 9, no. 2 (March 2001): 123–31. http://dx.doi.org/10.1255/jnirs.299.

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The feasibility of near infrared (NIR) reflectance spectroscopy in determining various soil constituents such as total organic carbon, total nitrogen, exchangeable potassium and available phosphorus has been investigated, to monitor their concentration during a long-term agronomic trial. Soil samples previously analysed by conventional chemical methods were scanned using a NIRSystems 5000 monochromator and spectra were treated using several algorithms. The first derivative of each NIR spectrum was used for all statistical analyses. Step-up, stepwise and modified partial least squares (MPLS) regression methods were applied to develop reliable calibration models between the NIR spectral data and the results of wet analyses. MPLS almost always gave the most successful calibrations. The results demonstrated that NIR reflectance spectroscopy can be used to determine accurately two important soil constituents, namely total nitrogen and carbon content. This technique could be employed as a routine testing method in estimating, rapidly and non-destructively, these constituents in soil samples, demonstrating soil variations within a long-term field experiment. For other determinations, such as exchangeable potassium and available phosphorus content, our results were less successful but may be useful for separation of samples into groups.
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10

Martínez-Valdivieso, Damián, Rafael Font, and Mercedes Del Río-Celestino. "Prediction of Agro-Morphological and Nutritional Traits in Ethiopian Mustard Leaves (Brassica Carinata A. Braun) by Visible-Near-Infrared Spectroscopy." Foods 8, no. 1 (December 22, 2018): 6. http://dx.doi.org/10.3390/foods8010006.

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The particular characteristics of some of the Ethiopian mustard accessions available from seed banks could be used to increase the production and the diversity of products available to consumers and to improve their general quality. The objectives of this study were to determine the genetic variability for agro-morphological (days to first flowering: DFF and leaf pubescence: LP) and nutritional traits (total phenolic content: TPC) among accessions, and to evaluate the potential of near-infrared spectroscopy (NIRS) to predict these traits in Ethiopian mustard leaves. A great variation was found for the traits evaluated. The reference values were regressed against different spectral transformations by modified partial least-squares (MPLS) regression. The coefficients of determination in cross-validation (R2cv) shown by the equations for DFF, LP and TPC were 0.95, 0.63 and 0.99, respectively. The standard deviation to standard error of cross-validation ratio (RPD), were for these traits, as follows: DFF: 4.52, LP: 1.53 and, TPC: 24.50. These results show that the equations developed for DFF and TPC in Ethiopian mustard, can be predicted with sufficient accuracy for screening purposes and quality control, respectively. In addition, the LP equation can be used to identify those samples with “low”, “medium” and “high” groups. From the study of the mean and deviation standard spectra, and regression vectors of MPLS models it can be concluded that some major cell components, highly participated in modelling the equations for these traits.
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