Journal articles: 'Modified partial least squares regression (MPLS)'

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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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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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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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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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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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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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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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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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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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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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Deaville, E. R., and D. L. Givens. "Factors affecting the prediction of organic matter Digestibility of grass silage by near infrared reflectance spectroscopy." Proceedings of the British Society of Animal Production (1972) 1994 (March 1994): 73. http://dx.doi.org/10.1017/s0308229600026209.

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Earlier studies (Barber et al., 1990) showed the superiority of near infrared reflectance spectroscopy (NIRS) for predicting the organic matter digestibility (OMD) in vivo of grass silage over fibre and in vitro procedures. However, during routine application occasional erroneous values were predicted for which there were no obvious reasons. Baker and Barnes (1990) reported that the likely sources of the problems contributing to the errors were instrumental and environmental noise, sample particle size effects and variable moisture content of the samples. These authors also reported that standard normal variate - detrend (SNV-D) scatter correction procedure of Barnes et al. (1989) could be used to reduce the effects of particle size variation and they also emphasised the need to test NIRS calibrations for repeatability. The purpose of the present work was to evaluate the use of the SNV-D scatter correction procedure, the techniques for reducing the sensitivity of calibrations to residual moisture and methods to improve the repeatability of the predicted OMD in vivo values of grass silage. In addition, a further objective was to compare three calibration methods, namely modified stepwise regression (MSR), modified partial least squares (MPLS) and principal component analysis (PCA).

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Guthrie, J., and K. Walsh. "Non-invasive assessment of pineapple and mango fruit quality using near infra-red spectroscopy." Australian Journal of Experimental Agriculture 37, no. 2 (1997): 253. http://dx.doi.org/10.1071/ea96026.

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Summary. The potential of near infra-red (NIR) spectroscopy for non-invasive measurement of fruit quality of pineapple (Ananas comosus var. Smooth Cayenne) and mango (Magnifera indica var. Kensington) fruit was assessed. A remote reflectance fibre optic probe, placed in contact with the fruit skin surface in a light-proof box, was used to deliver monochromatic light to the fruit, and to collect NIR reflectance spectra (760–2500 nm). The probe illuminated and collected reflected radiation from an area of about 16 cm2. The NIR spectral attributes were correlated with pineapple juice Brix and with mango flesh dry matter (DM) measured from fruit flesh directly underlying the scanned area. The highest correlations for both fruit were found using the second derivative of the spectra (d2 log 1/R) and an additive calibration equation. Multiple linear regression (MLR) on pineapple fruit spectra (n = 85) gave a calibration equation using d2 log 1/R at wavelengths of 866, 760, 1232 and 832 nm with a multiple coefficient of determination (R2) of 0.75, and a standard error of calibration (SEC) of 1.21 °Brix. Modified partial least squares (MPLS) regression analysis yielded a calibration equation with R2 = 0.91, SEC = 0.69, and a standard error of cross validation (SECV) of 1.09 oBrix. For mango, MLR gave a calibration equation using d2 log 1/R at 904, 872, 1660 and 1516 nm with R2 = 0.90, and SEC = 0.85% DM and a bias of 0.39. Using MPLS analysis, a calibration equation with R2 = 0.98, SEC = 0.54 and SECV = 1.19 was obtained. We conclude that NIR technology offers the potential to assess fruit sweetness in intact whole pineapple and DM in mango fruit, respectively, to within 1° Brix and 1% DM, and could be used for the grading of fruit in fruit packing sheds.

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Villamarín, Begoña, Esperanza Fernández, and Jesus Mendéz. "Analysis of Grass Silage from Northwestern Spain by Near-Infrared Reflectance Spectroscopy." Journal of AOAC INTERNATIONAL 85, no. 3 (May 1, 2002): 541–45. http://dx.doi.org/10.1093/jaoac/85.3.541.

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Abstract Near-infrared reflectance spectroscopy (NIRS) was evaluated for the determination of protein, crude fiber (CF), acid detergent fiber (ADF), and neutral detergent fiber (NDF) in grass silage. Calibration equations were based on analyses of 366 samples of grass silage produced in Northwestern Spain over 4 consecutive years (1992–1995) and validated by analyses of a set of 72 silage samples harvested during 1996. Dried and ground samples were analyzed by chemical and NIRS procedures. The spectral data were analyzed by regression against a range of chemical parameters, using modified partial least-squares (MPLS) multivariate analysis in conjunction with different mathematical treatments of the spectra. For each parameter, the optimum calibration was evaluated on the basis of the coefficient of multiple determination (R2), the coefficient of simple correlation (r2), the standard error of calibration (SEC), the standard error of cross-validation (SECV), and the standard error of validation (SEV). R2 and r2 were >0.90; SEC values were 0.58, 1.04, 1.40, and 1.75; SECV values were 0.64, 1.15, 1.50, and 2.04; and SEV values were 0.56, 1.02, 1.42, and 1.80 for protein, CF, ADF, and NDF, respectively. The ratio of the standard deviation of the reference data to the SEV was >3.0 for each of the 4 parameters, which indicates that the equations can be used in routine analysis.

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Oluk, Aylin Celile. "Estimation of Proximate, Fatty Acid, Mineral Content and Proline Level in Amaranth using Near Infrared Reflectance Spectroscopy." Sains Malaysiana 51, no. 10 (October 31, 2022): 3321–32. http://dx.doi.org/10.17576/jsm-2022-5110-17.

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For successful development of new amaranth varieties, it is important to find inexpensive and rapid analysis methods for the measurement of proximate, fatty acid, mineral content, and proline level in seeds. In this study, calibration equations in NIR spectroscopy were developed to estimate for the fatty acid, mineral content and proline level of amaranth using the modified partial least squares (MPLS) regression method. The calibrations estimated by NIR spectroscopy were consistent with the correlations between reference values at external validation. The equations developed were evaluated based on the relative estimate determination results for external validation (RPDv). The equations for total protein (RPDv = 2.967), fat (RPDv = 4.396), Zn (RPDv = 3.668), proline (RPDv = 6.692), oleic acid (RPDv = 3.366) and linoleic acid (RPDv = 2.086) showed high accuracy, while the equations for ash (RPDv = 1.675) and Fe (RPDv = 1.565) showed relatively high accuracy. When calculated with the same validation factors, the level of Ca (RPDv = 0.268), palmitic acid (RPDv = 1.434), stearic acid (RPDv = 0.949), linolenic acid (RPDv = 1.244) and arachidic acid (RPDv = 0.402) were lower than the estimated value. Protein, oil, ash, Fe, Zn, proline, oleic acid and linoleic acid can be used as reliable users, while equations developed for Ca, palmitic acid, stearic acid, linolenic acid and arachidic acid can be reliably used to screen samples for amaranth breeding programmes.

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MORON, A., and D. COZZOLINO. "Determination of potentially mineralizable nitrogen and nitrogen in particulate organic matter fractions in soil by visible and near-infrared reflectance spectroscopy." Journal of Agricultural Science 142, no. 3 (June 2004): 335–43. http://dx.doi.org/10.1017/s0021859604004290.

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Visible (VIS) and near-infrared reflectance spectroscopy (NIRS) combined with multivariate data analysis was used to predict potentially mineralizable nitrogen (PMN) and nitrogen in particulate organic matter fractions (PSOM-N). Soil samples from a long-term experiment (n=24) as well as soils under commercial management (n=160) in Uruguay (South America) were analysed. Samples were scanned in a NIRS 6500 monochromator instrument by reflectance (400–2500 nm). Modified partial least square regression (MPLS) and cross validation were used to develop the calibration models between NIRS data and reference values. NIRS calibration models gave a coefficient of determination for the calibration (R2CAL)>0·80 and the standard deviation of reference data to standard error in cross validation (RPD) ratio ranging from 2 to 5·5 for the variables evaluated. The results obtained in the study showed that NIRS could have the potential to determine PMN and PSOM-N fractions in soils under different agronomic conditions. However, the relatively limited number of samples led us to be cautious in terms of conclusions and to extend the results of this work to similar conditions.

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Guo, Tao, Luming Dai, Baipeng Yan, Guisheng Lan, Fadi Li, Fei Li, Faming Pan, and Fangbin Wang. "Measurements of Chemical Compositions in Corn Stover and Wheat Straw by Near-Infrared Reflectance Spectroscopy." Animals 11, no. 11 (November 22, 2021): 3328. http://dx.doi.org/10.3390/ani11113328.

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Rapid, non-destructive methods for determining the biochemical composition of straw are crucial in ruminant diets. In this work, ground samples of corn stover (n = 156) and wheat straw (n = 135) were scanned using near-infrared spectroscopy (instrument NIRS DS2500). Samples were divided into two sets, with one set used for calibration (corn stover, n = 126; wheat straw, n = 108) and the remaining set used for validation (corn stover, n = 30; wheat straw, n = 27). Calibration models were developed utilizing modified partial least squares (MPLS) regression with internal cross validation. Concentrations of moisture, crude protein (CP), and neutral detergent fiber (NDF) were successfully predicted in corn stover, and CP and moisture were in wheat straw, but other nutritional components were not predicted accurately when using single-crop samples. All samples were then combined to form new calibration (n = 233) and validation (n = 58) sets comprised of both corn stover and wheat straw. For these combined samples, the CP, NDF, and ADF were predicted successfully; the coefficients of determination for calibration (RSQC) were 0.9625, 0.8349, and 0.8745, with ratios of prediction to deviation (RPD) of 6.872, 2.210, and 2.751, respectively. The acid detergent lignin (ADL) and moisture were classified as moderately useful, with RSQC values of 0.7939 (RPD = 2.259) and 0.8342 (RPD = 1.868), respectively. Although the prediction of hemicellulose was only useful for screening purposes (RSQC = 0.4388, RPD = 1.085), it was concluded that NIRS is a suitable technique to rapidly evaluate the nutritional value of forage crops.

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Peters, Jenilee F., Mary Lou Swift, Gregory B. Penner, Bart Lardner, Timothy A. McAllister, and Gabriel O. Ribeiro. "PSVI-6 Predicting Fecal Composition Using Near Infrared Spectroscopy (Nirs): Expanding the Calibration to Include Grazing Beef Samples." Journal of Animal Science 100, Supplement_3 (September 21, 2022): 377–78. http://dx.doi.org/10.1093/jas/skac247.691.

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Abstract A near-infrared spectroscopy (NIRS) calibration was previously developed to predict fecal composition using samples from beef heifers fed high forage diets ( > 95% forage dry matter basis) during total collection digestibility studies. The objective of the current study was to expand the fecal composition calibration with samples from grazing beef cattle. Fecal samples were collected from beef steers grazing two annual and two perennial forage mixtures over 2 growing seasons. Individual samples (n = 12/paddock) were composited by paddock resulting in 30 samples from year 1, and 24 from year two. Fecal samples were oven dried at 55°C for 48 hours and ground through a 1.0 mm screen prior to scanning on a FOSS DS2500 scanning monochromator (FOSS, Eden Prairie, MN). The grazing fecal spectra (n = 54) was added to the existing library and then mathematically treated for scatter correction. Modified partial least squares (MPLS) regression was performed to develop equations to predict fecal composition [organic matter (OM), nitrogen (N), neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), undigestible NDF (uNDF), calcium (Ca), and phosphorus (P)]. The calibrations for fecal OM, N, NDF, ADF, ADL, uNDF, Ca, P resulted in R2CV between 0.86 and 0.96 and SECV of 1.73, 0.07, 1.65, 1.20, 0.63, 1.91, 0.21, and 0.07, respectively. This study confirms the potential of NIRS to predict fecal chemical composition of beef cattle fed high forage or grazing forage diets. Future steps include expansion and further validation of the calibration equations to include digestibility and intake predictions by estimating the internal markers lignin and uNDF.

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Peters, Jenilee F., Mary Lou Swift, Gregory B. Penner, Bart Lardner, Timothy A. McAllister, and Gabriel O. Ribeiro. "158 Predicting Intake and Digestibility of Nutrients in Beef Cattle fed High Forage Diets Using Near Infrared Spectroscopy (Nirs) of Feces and Internal Markers." Journal of Animal Science 100, Supplement_3 (September 21, 2022): 65–66. http://dx.doi.org/10.1093/jas/skac247.129.

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Abstract Objectives of this study were to determine the potential of near infrared-spectroscopy (NIRS) scanning of feces to predict diet digestibility and intake in beef cattle fed high forage diets, and to investigate the use of acid detergent lignin (ADL) and undigestible neutral detergent fiber (uNDF) as internal markers to determine digestibility. Beef heifers were fed 12 different forage-based diets ( > 95% forage dry matter basis) in 3 total collection studies, resulting in individual fecal samples and related spectra (n = 135) corresponding to apparent total tract digestibility (aTTD) and intake data. Dried and ground fecal samples were scanned using a FOSS DS2500 scanning monochromator (FOSS, Eden Prairie, MN). Modified partial least squares (MPLS) regression was performed to develop equations to predict digestibility [dry matter (DM), organic matter (OM), NDF, and nitrogen (N)], and intake [DM, OM, NDF, N, uNDF]. Digestibility prediction equations for DM, OM, NDF, and N resulted in R2CV or 0.72, 0.65, 0.74 and 0.69, respectively, and standard error of cross validation (SECV) between 2.20 to 2.82. Intake calibrations for DM, OM, N, NDF, ADL and uNDF resulted in R2CV values between 0.59 and 0.91, SECV for intake (kg/d) of 1.12, 1.10, 0.02, 0.69, 0.06, 0.24, respectively, SECV for intake % body weight (BW) between 0.00 and 0.16; and SECV for g/kg BW0.75 between 0.60 and 0.86. Significant correlations (P < 0.01) were identified between NDFD determined by aTTD and by the internal marker ADL (r = 0.72) or uNDF (r = 0.57). Developed calibrations predicting marker calculated digestibility resulted in R2CV(SECV) values of 0.85(2.70) and 0.80(2.46) for lignin and uNDF, respectively. We confirm the potential of NIRS to predict digestibility and intake of cattle fed high forage diets. Future steps include further validation of the intake calibration equations using internal marker and energetics modeling.

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Shenk, John S., and Mark O. Westerhaus. "Populations Structuring of Near Infrared Spectra and Modified Partial Least Squares Regression." Crop Science 31, no. 6 (November 1991): 1548–55. http://dx.doi.org/10.2135/cropsci1991.0011183x003100060034x.

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Yin, Shen, Xiangping Zhu, and Hamid Reza Karimi. "Quality Evaluation Based on Multivariate Statistical Methods." Mathematical Problems in Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/639652.

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Quality prediction models are constructed based on multivariate statistical methods, including ordinary least squares regression (OLSR), principal component regression (PCR), partial least squares regression (PLSR), and modified partial least squares regression (MPLSR). The prediction model constructed by MPLSR achieves superior results, compared with the other three methods from both aspects of fitting efficiency and prediction ability. Based on it, further research is dedicated to selecting key variables to directly predict the product quality with satisfactory performance. The prediction models presented are more efficient than tradition ones and can be useful to support human experts in the evaluation and classification of the product quality. The effectiveness of the quality prediction models is finally illustrated and verified based on the practical data set of the red wine.

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Zhang, Yingwei, and Yang Zhang. "Complex process monitoring using modified partial least squares method of independent component regression." Chemometrics and Intelligent Laboratory Systems 98, no. 2 (October 2009): 143–48. http://dx.doi.org/10.1016/j.chemolab.2009.06.001.

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Yin, Shen, Lei Liu, Xin Gao, and Hamid Reza Karimi. "Multivariate Methods Based Soft Measurement for Wine Quality Evaluation." Abstract and Applied Analysis 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/740754.

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Soft measurement is a new, developing, and promising industry technology and has been widely used in the industry nowadays. This technology plays a significant role especially in the case where some key variables are difficult to be measured by traditional measurement methods. In this paper, the quality of the wine is evaluated given the wine physicochemical indexes according to multivariate methods based soft measurement. The multivariate methods used in this paper include ordinary least squares regression (OLSR), principal component regression (PCR), partial least squares regression (PLSR), and modified partial least squares regression (MPLSR). By comparing the performance of the four methods, the MPLSR prediction model shows superior results than the others. In general, to determine the quality of the wine, experienced wine tasters are hired to taste the wine and make a decision. However, since the physicochemical indexes of wine can to some extent reflect the quality of wine, the multivariate statistical methods based soft measure can help the oenologist in wine evaluation.

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Cozzolino, D., A. La Manna, and D. Vaz Martins. "Use of near Infrared Reflectance Spectroscopy to Analyse Bovine Faecal Samples." Journal of Near Infrared Spectroscopy 10, no. 4 (October 2002): 309–14. http://dx.doi.org/10.1255/jnirs.347.

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Near infrared (NIR) reflectance spectroscopy was used to predict nitrogen (N), acid detergent fibre (ADF), neutral detergent fibre (NDF) and chromium (Cr) in beef faecal samples. One hundred and twenty faecal samples were scanned in a NIRSystems 6500 monochromator instrument over the wavelength range of 400–2500 nm in reflectance. Calibration equations were developed using modified partial least squares (MPLS) with internal cross validation to avoid overfitting. The coefficient of determination in calibration ( R2cal) and the standard error in cross validation ( SECV) were 0.80 (0.74) for N, 0.92 (12.04) for ADF, 0.86 (13.5) for NDF and 0.56 (0.07) for Cr in g kg−1 dry weight, respectively. Results for validation were 0.78 ( SEP: 0.1) for N, 0.74 ( SEP: 7.5) for ADF, 0.85 ( SEP: 8.5) for NDF and 0.10 (0.09) for Cr in g kg−1 dry weight, respectively.

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Cheng, Hao. "Second Order Model with Composite Quantile Regression." Journal of Physics: Conference Series 2437, no. 1 (January 1, 2023): 012070. http://dx.doi.org/10.1088/1742-6596/2437/1/012070.

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Abstract In many fields, comprehensive evaluation is a very important topic. Both second order model and its modified one with quantile regression have been widely used in evaluation. Although quantile-type second order model has abilities in capturing a complete picture of different variables’ relations at different quantile levels. Sometimes we find it complex to summarize the conclusions of all quantile levels. Therefore, we propose a new second order model with composite quantile regression, which has the advantage of obtaining a whole evaluation result based on all quantile levels. More specifically, one of our paper’s main contributions is to develop a comprehensive evaluation model based on the classical second order model, quantile regression and composite quantile regression. What’s more, we introduce a modified partial least square estimation algorithm under the well-known partial least squares framework. The new algorithm has the ability in estimating both path and loading coefficients reflecting the relationships among manifest variables and latent variables at all quantile levels.

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Font, Rafael, Mercedes del Río-Celestino, Diego Luna, Juan Gil, and Antonio de Haro-Bailón. "Rapid and Cost-Effective Assessment of the Neutral and Acid Detergent Fiber Fractions of Chickpea (Cicer arietinum L.) by Combining Modified PLS and Visible with Near-Infrared Spectroscopy." Agronomy 11, no. 4 (April 1, 2021): 666. http://dx.doi.org/10.3390/agronomy11040666.

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The near-infrared spectroscopy (NIRS) combined with modified partial least squares (modified PLS) regression was used for determining the neutral detergent fiber (NDF) and the acid detergent fiber (ADF) fractions of the chickpea (Cicer arietinum L.) seed. Fifty chickpea accessions (24 desi and 26 kabuli types) and fifty recombinant inbred lines F5:6 derived from a kabuli × desi cross were evaluated for NDF and ADF, and scanned by NIRS. NDF and ADF values were regressed against different spectral transformations by modified partial least squares regression. The coefficients of determination in the cross-validation and the standard deviation from the standard error of cross-validation ratio were, for NDF, 0.91 and 3.37, and for ADF, 0.98 and 6.73, respectively, showing the high potential of NIRS to assess these components in chickpea for screening (NDF) or quality control (ADF) purposes. The spectral information provided by different chromophores existing in the chickpea seed highly correlated with the NDF and ADF composition of the seed, and, thus, those electronic transitions are highly influenced on model fitting for fiber.

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Ren, Zelin, Yongqiang Tang, and Wensheng Zhang. "Quality-related fault diagnosis based on k-nearest neighbor rule for non-linear industrial processes." International Journal of Distributed Sensor Networks 17, no. 11 (November 2021): 155014772110559. http://dx.doi.org/10.1177/15501477211055931.

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The fault diagnosis approaches based on k-nearest neighbor rule have been widely researched for industrial processes and achieve excellent performance. However, for quality-related fault diagnosis, the approaches using k-nearest neighbor rule have been still not sufficiently studied. To tackle this problem, in this article, we propose a novel quality-related fault diagnosis framework, which is made up of two parts: fault detection and fault isolation. In the fault detection stage, we innovatively propose a novel non-linear quality-related fault detection method called kernel partial least squares- k-nearest neighbor rule, which organically incorporates k-nearest neighbor rule with kernel partial least squares. Specifically, we first employ kernel partial least squares to establish a non-linear regression model between quality variables and process variables. After that, the statistics and thresholds corresponding to process space and predicted quality space are appropriately designed by adopting k-nearest neighbor rule. In the fault isolation stage, in order to match our proposed non-linear quality-related fault detection method kernel partial least squares- k-nearest neighbor seamlessly, we propose a modified variable contributions by k-nearest neighbor (VCkNN) fault isolation method called modified variable contributions by k-nearest neighbor (MVCkNN), which elaborately introduces the idea of the accumulative relative contribution rate into VC k-nearest neighbor, such that the smearing effect caused by the normal distribution hypothesis of VC k-nearest neighbor can be mitigated effectively. Finally, a widely used numerical example and the Tennessee Eastman process are employed to verify the effectiveness of our proposed approach.

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Han, Liang, Feng Liu, and Li Zhang. "An Improved Sub-Model PLSR Quantitative Analysis Method Based on SVM Classifier for ChemCam Laser-Induced Breakdown Spectroscopy." Symmetry 13, no. 2 (February 15, 2021): 319. http://dx.doi.org/10.3390/sym13020319.

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Laser-induced breakdown spectroscopy (LIBS) is a powerful tool for qualitative and quantitative analysis. Component analysis is a significant issue for the LIBS instrument onboard the Mars Science Laboratory (MSL) rover Curiosity ChemCam and SuperCam on the Mars 2020 rover. The partial least squares (PLS) sub-model strategy is one of the outstanding multivariate analysis methods for calibration modeling, which is firstly developed by the ChemCam science team. We innovatively used a support vector machine (SVM) classifier to select the corresponding sub-model. Then conventional regression approaches partial least squares regression (PLSR) was utilized as a sub-model to prove that our selecting method was feasible, effective, and well-performed. For eight oxides, i.e., SiO2, TiO2, Al2O3, FeOT, MgO, CaO, Na2O, and K2O, the modified SVM-PLSR blended sub-model method was 34.8% to 62.4% lower than the corresponding root mean square error of prediction (RMSEP) of the full model method. In order to avoid that SVM classifiers classifying the spectrum into an incorrect class, an optimized method was proposed which worked well in the modified PLSR blended sub-models.

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Lee, So-Yoon, Myung-Hoe Huh, and Mira Park. "A modified partial least squares regression for the analysis of gene expression data with survival information." Journal of the Korean Data and Information Science Society 25, no. 5 (September 30, 2014): 1151–60. http://dx.doi.org/10.7465/jkdi.2014.25.5.1151.

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Martens, Harald, and Magni Martens. "Modified Jack-knife estimation of parameter uncertainty in bilinear modelling by partial least squares regression (PLSR)." Food Quality and Preference 11, no. 1-2 (January 2000): 5–16. http://dx.doi.org/10.1016/s0950-3293(99)00039-7.

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Phillips, Peter C. B. "Robust Nonstationary Regression." Econometric Theory 11, no. 5 (October 1995): 912–51. http://dx.doi.org/10.1017/s0266466600009920.

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This paper provides a robust statistical approach to nonstationary time series regression and inference. Fully modified extensions of traditional robust statistical procedures are developed that allow for endogeneities in the nonstationary regressors and serial dependence in the shocks that drive the regressors and the errors that appear in the equation being estimated. The suggested estimators involve semiparametric corrections to accommodate these possibilities, and they belong to the same family as the fully modified least-squares (FM-OLS) estimator of Phillips and Hansen (1990, Review of Economic Studies 57,99–125). Specific attention is given to fully modified least absolute deviation (FM-LAD) estimation and fully modified M (FM-M) estimation. The criterion function for LAD and some M-estimators is not always smooth, and this paper develops generalized function methods to cope with this difficulty in the asymptotics. The results given here include a strong law of large numbers and some weak convergence theory for partial sums of generalized functions of random variables. The limit distribution theory for FM-LAD and FM-M estimators that is developed includes the case of finite variance errors and the case of heavytailed (infinite variance) errors. Some simulations and a brief empirical illustration are reported.

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Omitaomu, Olufemi A., Myong K. Jeong, Adedeji B. Badiru, and J. Wesley Hines. "On-Line Prediction of Motor Shaft Misalignment Using Fast Fourier Transform Generated Spectra Data and Support Vector Regression." Journal of Manufacturing Science and Engineering 128, no. 4 (February 3, 2006): 1019–24. http://dx.doi.org/10.1115/1.2194059.

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Shaft alignment prediction is essential for the development of effective coupling and rotating equipment maintenance systems. In this paper, we present a modified support vector regression (SVR) approach for shaft alignment predictions based on fast Fourier transform generated spectra data. The modified SVR approach uses data-dependent parameters in order to reduce computation time and achieve better predictions. The spectra data used is characterized by a large number of descriptors and very few data points. The strengths of SVR for shaft misalignment prediction include its ability to represent data in high-dimensional space through kernel functions. We reduce the dimension of the data using a multivariate AIC criterion in order to guarantee that the selected spectra are response dependent. We compare the performance of SVR with two of the most popular techniques used in condition monitoring, partial least squares, and principal components regression. Our results show that we can improve the performance of shaft misalignment prediction using SVR and the approach compares very favorably with partial least squares and principal components regression approaches. Also, we present a quantitative measure, shaft misalignment monitoring index, which can be used to facilitate easy identification of the alignment condition and as input to maintenance systems design.

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Chen, Da, Bin Hu, Xueguang Shao, and Qingde Su. "Variable selection by modified IPW (iterative predictor weighting)-PLS (partial least squares) in continuous wavelet regression models." Analyst 129, no. 7 (2004): 664. http://dx.doi.org/10.1039/b400410h.

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Perez-Guaita, David, Julia Kuligowski, Guillermo Quintás, Salvador Garrigues, and Miguel de la Guardia. "Modified locally weighted—Partial least squares regression improving clinical predictions from infrared spectra of human serum samples." Talanta 107 (March 2013): 368–75. http://dx.doi.org/10.1016/j.talanta.2013.01.035.

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34

Morón, A., and D. Cozzolino. "Application of near Infrared Reflectance Spectroscopy for the Analysis of Organic C, Total N and pH in Soils of Uruguay." Journal of Near Infrared Spectroscopy 10, no. 3 (June 2002): 215–21. http://dx.doi.org/10.1255/jnirs.338.

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Near infrared (NIR) reflectance spectroscopy was used to analyse samples ( n = 332) from different soils from Uruguay (South America) for organic carbon (OC), total nitrogen (N) and pH. One set ( n = 200) of samples randomly selected was used to develop the NIR calibrations while the remaining ( n = 132) samples were used as the validation set. The samples were scanned in a small circular cup in reflectance mode (400–2500 nm), using a Foss NIRSystems 6500 (Silver Spring, MD, USA). Modified partial least squares (MPLS) was used to produce the calibration models and cross-validation was used to avoid collinearity effects among variables. Three mathematical treatments and four scatter corrections were also applied. The calibration coefficient of determination ( R2CAL) and the standard error in cross-validation ( SECV) were 0.94 ( SECV: 1.9) for OC; 0.91 ( SECV: 0.19) for total N in g kg−1 and 0.93 ( SECV: 0.18) for pH, respectively. The simple correlation coefficient of validation ( rVAL) and the standard errors of prediction ( SEP) were 0.74 and 5; 0.73 and 0.4; 0.84 and 0.28 for OC, total N and pH, respectively.

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MORÓN, A., and D. COZZOLINO. "Determination of macro elements in alfalfa and white clover by near-infrared reflectance spectroscopy." Journal of Agricultural Science 139, no. 4 (December 2002): 413–23. http://dx.doi.org/10.1017/s0021859602002605.

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Near-infrared reflectance spectroscopy was used to assess the mineral composition of both alfalfa (Medicago sativa L.) and white clover (Trifolium repens L.). Alfalfa (n=230) and white clover (n=97) plant samples from different locations in Uruguay representing a wide range of soil types were analysed. The samples were scanned for reflectance in a NIRSystems 6500 monochromator (NIRSystems, Silver Spring, MD, USA). Predictive equations were developed using modified partial least squares (MPLS) with cross validation to avoid overfitting. The coefficients of determination in calibration (R_{\rm cal}^{2}) and the standard errors in cross validation (SECV) were 0·93 (SECV: 1·6), 0·95 (SECV: 1·3), 0·93 (SECV: 1·9), 0·88 (SECV: 2·7), 0·82 (SECV: 0·3) and 0·75 (SECV: 4·7) for alfalfa and 0·98 (SECV: 0·8), 0·52 (SECV: 0·8), 0·97 (SECV: 2·7), 0·83 (SECV: 3·1), 0·82 (SECV: 1·9) and 0·45 (SECV: 2·6) for white clover, for N, Ca, K, P, Mg and S in g/kg on a dry weight respectively. Calcium, nitrogen and potassium were well predicted by NIRS in both alfalfa and white clover samples.

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Moron, A., and D. Cozzolino. "Exploring the Use of near Infrared Reflectance Spectroscopy to Study Physical Properties and Microelements in Soils." Journal of Near Infrared Spectroscopy 11, no. 2 (April 2003): 145–54. http://dx.doi.org/10.1255/jnirs.362.

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Near infrared (NIR) reflectance spectroscopy was used to predict the content of silt, sand, clay, iron (Fe), copper (Cu), manganese (Mn) and zinc (Zn) in soil. A total of 332 samples from agricultural soils (0–15 cm depth) in Uruguay (South America) were used. The samples were scanned in a monochromator instrument (NIRSystems 6500, Silver Spring, MD, USA). Two mathematical treatments (first and second derivative) with SNVD (scatter normal variate and detrend) and without scatter correction were studied. Modified partial least squares (mPLS) was used to develop the calibration models. The coefficient of determination in calibration ( R2cal) and the standard error in calibration ( SEC) using the second derivative were 0.81 ( SEC: 5.1), 0.83 ( SEC: 5.3), 0.92 ( SEC: 2.6) for percent sand, silt and clay, respectively. The R2cal and standard error of cross-validation ( SECV) were for Cu 0.87 ( SEC: 0.7), for Fe 0.92 ( SEC: 21.7), for Mn 0.72 ( SEC: 83.0) and for Zn 0.72 ( SEC: 1.2) on mg kg−1 dry matter. It was concluded that NIR reflectance spectroscopy has a great potential as an analytical method for routine analysis of soil texture, Fe, Zn and Cu due the speed and low cost of analysis.

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Pavlov, Alexander, and Maxim Holovchenko. "MODIFIED METHOD OF CONSTRUCTING A MULTIVARIATE LINEAR REGRESSION GIVEN BY A REDUNDANT DESCRIPTION." Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies, no. 2 (8) (December 23, 2022): 3–8. http://dx.doi.org/10.20998/2079-0023.2022.02.01.

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A number of scientific works of Prof. O. A. Pavlov and his disciples is devoted to the development of an original method of efficient estimation of coefficients at nonlinear terms of multivariate polynomial regression given by a redundant description under the conditions of an active experiment. The solution of the formulated problem is reduced to the sequential construction of univariate polynomial regressions (finding efficient estimates for the coefficients at nonlinear terms) and solving the corresponding systems of linear nondegenerate equations, the variables of which are the estimates for coefficients at nonlinear terms of the multivariate polynomial regression given by the redundant description. Thus, the problem was reduced to the estimation of the coefficients at linear terms of a multivariate linear regression given by a redundant description in the conditions of an active experiment. We have proposed an original method of its solution that uses a cluster analysis algorithm. The algorithm’s implementation significantly reduces the enumeration of partial descriptions of multivariate linear regression followed by the finding of the residual sum of squares for each of them. This allows using the chi-squared criterion to build a linguistic variable which value gives a qualitative assessment (high reliability, acceptable reliability, low reliability, unreliability) to the obtained result. The analysis of the computational experiments made it possible to modify the proposed method, which significantly increased its efficiency, first of all, of finding a reliable structure of the sought multivariate linear regression given by the redundant description. The method modification, in particular, has reduced the enumeration of partial descriptions and has led to a more efficient use of the general procedure of the least squares method.

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Zhang, Yue, Yan Zhou, Shujun Chen, Yashi You, Ping Qiu, and Yongnian Ni. "Analysis of the Overlapped Electrochemical Signals of Hydrochlorothiazide and Pyridoxine on the Ethylenediamine-Modified Glassy Carbon Electrode by Use of Chemometrics Methods." Molecules 24, no. 14 (July 11, 2019): 2536. http://dx.doi.org/10.3390/molecules24142536.

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In this work, the electrochemical behavior of hydrochlorothiazide and pyridoxine on the ethylenediamine-modified glassy carbon electrode were investigated by differential pulse voltammetry. In pH 3.4 Britton-Robinson (B-R) buffer solution, both hydrochlorothiazide and pyridoxine had a pair of sensitive irreversible oxidation peaks, that overlapped in the 1.10 V to 1.20 V potential range. Under the optimum experimental conditions, the peak current was linearly related to hydrochlorothiazide and pyridoxine in the concentration range of 0.10–2.0 μg/mL and 0.02–0.40 μg/mL, respectively. Chemometrics methods, including classical least squares (CLS), principal component regression (PCR) and partial least squares (PLS), were introduced to resolve the overlapped signals and determine the two components in mixtures, which avoided the troublesome steps of separation and purification. Finally, the simultaneous determination of the two components in commercial pharmaceuticals was performed with satisfactory results.

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Ortiz, Alberto, Lucía León, Rebeca Contador, and David Tejerina. "Near-Infrared Spectroscopy (NIRS) as a Tool for Classification of Pre-Sliced Iberian Salchichón, Modified Atmosphere Packaged (MAP) According to the Official Commercial Categories of Raw Meat." Foods 10, no. 8 (August 12, 2021): 1865. http://dx.doi.org/10.3390/foods10081865.

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This study evaluates near-infrared spectroscopy (NIRS) feasibility in combination with various pre-treatments and chemometric approaches for pre-sliced Iberian salchichón under modified atmosphere (MAP) classification according to the official commercial category (defined by the combination of genotype and feeding regime) of the raw material used for its manufacturing (Black and Red purebred Iberian and Iberian × Duroc crossed (50%) pigs, respectively, reared outdoors in a Montanera system and White Iberian × Duroc crossed (50%) pigs with feed based on commercial fodder) without opening the package. In parallel, NIRS feasibility in combination with partial least squares regression (PLSR) to predict main quality traits was assessed. The best-fitting models developed by means of partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) yielded high discriminant ability and thus offered a tool to support the assignment of pre-sliced MAP Iberian salchichón according to the commercial category of the raw material. In addition, good predictive ability for C18:3 n-3 was obtained, which may help to support quality control.

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Cozzolino, D., and I. Murray. "Effect of Sample Presentation and Animal Muscle Species on the Analysis of Meat by near Infrared Reflectance Spectroscopy." Journal of Near Infrared Spectroscopy 10, no. 1 (January 2002): 37–44. http://dx.doi.org/10.1255/jnirs.319.

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The useful wavelengths in both the visible and the near infrared region as well as two sample presentations (intact and minced) were evaluated to assess moisture (M), crude protein (CP) and intra muscular fat (IMF) in lamb ( n = 300), beef ( n = 100) and chicken ( n = 48) muscle samples. Samples were scanned in reflectance in a NIRSystems 6500 (NIRSystems, Silver Spring, MD, USA). Predictive equations were performed using modified partial least squares (MPLS) with internal cross-validation. The coefficient of determination in calibration ( R2CAL) and the standard error in cross-validation ( SECV) were calculated for each chemical parameter. For moisture, crude protein and fat (each expressed as g kg−1), R2CAL and SECV for beef muscle were 0.98, 0.81 and 0.96, respectively, and SECV was 33.1, 21.8 and 44.8 for beef muscle; for chicken muscle the comparable statistics were 0.99, 0.97 and 0.95 and SECV was 6.9, 2.4 and 33.1; while for lamb muscle R2CAL was 0.76, 0.83 and 0.73 and SECV 10.3, 5.5 and 4.7. It was concluded that the minced presentation is the best way to analyse muscle samples. On the other hand, intact presentation could have a great potential for use in the meat industry, although more research will be needed in order to determine quality attributes on meat samples.

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Van De Voort, Frederick R., Jacqueline Sedman, Gary Emo, and Ashraf A. Ismail. "Assessment of Fourier Transform Infrared Analysis of Milk." Journal of AOAC INTERNATIONAL 75, no. 5 (September 1, 1992): 780–85. http://dx.doi.org/10.1093/jaoac/75.5.780.

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Abstract A Nicolet 510 Fourier transform infrared (FTIR) spectrometer was modified to perform IR milk analyses by incorporating a temperature-controlled 37 µm CaF2 flow cell and a homogenizer into the analytical system. The unit was evaluated for its ability to predict the chemical values of calibration milks, and its performance was compared with that of a commercial filter-based IR milk analyzer (Multispec MK1). Conventional dual-wavelength multiple regression methods and whole-spectrum multivariate analysis techniques (classical least squares and partial least squares) were also compared for their predictive capabilities. We found that the prototype FTIR unit met AOAC standards for milk analyses and performed as well as or better than the filter instrument. The macro-programming capability of the FTIR software, which enables the user to combine strings of FTIR commands and parameters into a single command, allowed automation of data processing. The whole-spectrum multivariate analysis methods were able to provide total solids data directly in addition to fat, protein, and lactose. On the basis of this evaluation, FTIR appears to be a viable alternative means for performing milk analyses.

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Xu, Jinfeng. "High-Dimensional Cox Regression Analysis in Genetic Studies with Censored Survival Outcomes." Journal of Probability and Statistics 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/478680.

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With the advancement of high-throughput technologies, nowadays high-dimensional genomic and proteomic data are easy to obtain and have become ever increasingly important in unveiling the complex etiology of many diseases. While relating a large number of factors to a survival outcome through the Cox relative risk model, various techniques have been proposed in the literature. We review some recently developed methods for such analysis. For high-dimensional variable selection in the Cox model with parametric relative risk, we consider the univariate shrinkage method (US) using the lasso penalty and the penalized partial likelihood method using the folded penalties (PPL). The penalization methods are not restricted to the finite-dimensional case. For the high-dimensional (p→∞,p≪n) or ultrahigh-dimensional case (n→∞,n≪p), both the sure independence screening (SIS) method and the extended Bayesian information criterion (EBIC) can be further incorporated into the penalization methods for variable selection. We also consider the penalization method for the Cox model with semiparametric relative risk, and the modified partial least squares method for the Cox model. The comparison of different methods is discussed and numerical examples are provided for the illustration. Finally, areas of further research are presented.

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Ortiz-Barrios, Miguel, Eric Järpe, Matías García-Constantino, Ian Cleland, Chris Nugent, Sebastián Arias-Fonseca, and Natalia Jaramillo-Rueda. "Predicting Activity Duration in Smart Sensing Environments Using Synthetic Data and Partial Least Squares Regression: The Case of Dementia Patients." Sensors 22, no. 14 (July 20, 2022): 5410. http://dx.doi.org/10.3390/s22145410.

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The accurate recognition of activities is fundamental for following up on the health progress of people with dementia (PwD), thereby supporting subsequent diagnosis and treatments. When monitoring the activities of daily living (ADLs), it is feasible to detect behaviour patterns, parse out the disease evolution, and consequently provide effective and timely assistance. However, this task is affected by uncertainties derived from the differences in smart home configurations and the way in which each person undertakes the ADLs. One adjacent pathway is to train a supervised classification algorithm using large-sized datasets; nonetheless, obtaining real-world data is costly and characterized by a challenging recruiting research process. The resulting activity data is then small and may not capture each person’s intrinsic properties. Simulation approaches have risen as an alternative efficient choice, but synthetic data can be significantly dissimilar compared to real data. Hence, this paper proposes the application of Partial Least Squares Regression (PLSR) to approximate the real activity duration of various ADLs based on synthetic observations. First, the real activity duration of each ADL is initially contrasted with the one derived from an intelligent environment simulator. Following this, different PLSR models were evaluated for estimating real activity duration based on synthetic variables. A case study including eight ADLs was considered to validate the proposed approach. The results revealed that simulated and real observations are significantly different in some ADLs (p-value < 0.05), nevertheless synthetic variables can be further modified to predict the real activity duration with high accuracy (R2(pred)>90%).

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Çelen, İpek, David Harper, and Nicole Labbé. "A multivariate approach to the acetylated poplar wood samples by near infrared spectroscopy." Holzforschung 62, no. 2 (March 1, 2008): 189–96. http://dx.doi.org/10.1515/hf.2008.048.

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Abstract Yellow poplar (Liriodendron tulipifera L.) wood flour was chemically modified with acetic anhydride at varying times and initial moisture contents at a constant temperature. All samples exhibited increasing weight gains in the range of 5–16% with increasing reaction or treatment time. The acetylated poplar wood flour was characterized by means of Fourier transform infrared spectroscopy and near infrared (NIR) spectroscopy. Principal component analysis performed on the spectral data revealed clusters according to the esterification level. Partial least squares regression models developed from NIR data were able to predict the weight gain and the percentage of reacted OH groups. The correlations show that there is a direct and linear relationship between the spectra and the weight percentage gain and, therefore, the degree of acetylation.

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Kráčmar, S., R. Jankovská, K. Šustová, J. Kuchtík, and L. Zeman. "Analysis of amino acid composition of sheep colostrum by near-infrared spectroscopy." Czech Journal of Animal Science 49, No. 5 (December 12, 2011): 177–82. http://dx.doi.org/10.17221/4297-cjas.

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This paper deals with changes in the basic composition of sheep colostrum within the first 72 hours after parturition on the one hand and with the possibility of determining the major components of sheep colostrum by near-infrared spectroscopy on the other. Levels of essential, nonessential and total amino acids in sheep colostrum were determined by near-infrared reflectance spectroscopy (NIRS). ). For each component, sets of 90 samples were used to calibrate the instrument by means of a modified partial least-squares regression. The values of correlation coefficients (r) were as follows: 0.979 for Thr; 0.954 for Val; 0.968 for Leu; 0.918 for Ile; 0.946 for Lys; 0.908 for Arg; 0.845 for His; 0.999 for Trp; 0.915 for Phe; 0.909 for Met; 0.939 for Cys; 0.911 for Σmet + Cys; 0.933 for Tyr; 0.945 for Asp; 0.935 for Glu; 0.986 for Ser; 0.985 for Pro; 0.957 for Gly; 0.949 for Ala; 0.940 for ΣEAA; 0.958 for ΣNEAA and 0.977 for ΣAA. Partial least-squares (PLS) regression was used to develop calibration models for examined samples of sheep colostrum. When using the NIRS method, the following correlation coefficients were calculated: Thr (0.959), Val (0.912), Leu (0.936), Ile (0.855), Lys (0.903), Arg (0.853), His (0.717), Trp (0.667), Phe (0.854), Met (0.867), Cys (0.895), Σmet + Cys (0.868), Tyr (0.886), Asp (0.910), Glu (0.882), Ser (0.968), Pro (0.968), Gly (0.923), Ala (0.916), ΣEAA (0.901), ΣNEAA (0.923) and ΣAA (0.943). Calibration was tested using the same set of samples.NIRS results were compared with reference data and no significant differences between them were found (P = 0.05). Calibration and validation models were constructed in the same way.Results of this study indicate that NIR spectroscopy can be used for a rapid analysis of amino acid contents in sheep colostrum.  

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Bahram, Morteza, Khalil Farhadi, and Farzin Arjmand. "Voltammetric determination of dopamine in the presence of ascorbic and uric acids using partial least squares regression: determination of dopamine in human urine and plasma." Open Chemistry 7, no. 3 (September 1, 2009): 524–31. http://dx.doi.org/10.2478/s11532-009-0057-4.

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AbstractA new differential pulse voltammetric method for dopamine determination at a bare glassy carbon electrode has been developed. Dopamine, ascorbic acid (AA) and uric acid (UA) usually coexist in physiological samples. Because AA and UA can be oxidized at potentials close to that of DA it is difficult to determine dopamine electrochemically, although resolution can be achieved using modified electrodes. Additionally, oxidized dopamine mediates AA oxidation and the electrode surface can be easily fouled by the AA oxidation product. In this work a chemometrics strategy, partial least squares (PLS) regression, has been applied to determine dopamine in the presence of AA and UA without electrode modification. The method is based on the electrooxidation of dopamine at a glassy carbon electrode in pH 7 phosphate buffer. The dopamine calibration curve was linear over the range of 1–313 μM and the limit of detection was 0.25 μM. The relative standard error (RSE %) was 5.28%. The method has been successfully applied to the measurement of dopamine in human plasma and urine.

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Hermida, MarÍa, Natalia Rodriguez, and Jose L. Rodriguez-Otero. "Determination of Moisture, Starch, Protein, and Fat in Common Beans (Phaseolus vulgaris L.) by Near Infrared Spectroscopy." Journal of AOAC INTERNATIONAL 89, no. 4 (July 1, 2006): 1039–41. http://dx.doi.org/10.1093/jaoac/89.4.1039.

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Abstract The presence of moisture, starch, protein, and fat was determined in common beans (Phaseolus vulgaris L.) by near infrared (NIR) spectroscopy without any previous sample pretreatment except grinding. A set of 96 samples was used to calibrate the instrument by modified partial least-squares regression. The following statistical results were achieved: standard error of calibration (SEC) = 0.31 and square correlation coefficient (R2) = 0.96 for moisture; SEC = 0.76 and R2 = 0.92 for starch; SEC = 0.39 and R2 = 0.98 for protein; and SEC = 0.14 and R2 = 0.80 for fat. To validate the calibration, a set of 25 bean samples was used. Standard errors of prediction were 0.39, 0.90, 0.56, and 0.13 for moisture, starch, protein, and fat, respectively, and R2 for the regression of measurements by the reference method versus NIR analysis were 0.94, 0.88, 0.94, and 0.74 for moisture, starch, protein, and fat, respectively. To compare the results obtained for all 4 components of the validation set by NIR spectroscopy with those obtained by the reference methods, linear regression and paired t tests were applied, and the methods did not give significantly different results, P = 0.05.

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Nasir, Vahid, Sepideh Nourian, Stavros Avramidis, and Julie Cool. "Prediction of physical and mechanical properties of thermally modified wood based on color change evaluated by means of “group method of data handling” (GMDH) neural network." Holzforschung 73, no. 4 (April 24, 2019): 381–92. http://dx.doi.org/10.1515/hf-2018-0146.

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AbstractThe effect of thermal modification (TM) on the color of western hemlock wood and its physical and mechanical properties were investigated. The focus of this study was the prediction of material properties of thermally modified wood based on the color change via the “group method of data handling (GMDH)” neural network (NN). The NN was trained by color parameters for predicting the equilibrium moisture content (EMC), density, porosity, water absorption (WA), swelling coefficient, dynamic modulus of elasticity (MOEdyn) and hardness. The color parameters showed a significant correlation with temperature and are well correlated with the heat treatment (HT) intensity. Color parameters combined with the GMDH-type NN successfully predicted the physical properties of the material. The best correlation was achieved with the swelling coefficient, EMC and WA. All these properties were significantly influenced by HT. The color parameters did not seem suitable for predicting the wood hardness and MOEdyn. The GMDH NN shows a higher model accuracy than the multivariate linear and partial least squares (PLS) regression models.

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Escuredo, Olga, Laura Meno, María Shantal Rodríguez-Flores, and Maria Carmen Seijo. "Rapid Estimation of Potato Quality Parameters by a Portable Near-Infrared Spectroscopy Device." Sensors 21, no. 24 (December 9, 2021): 8222. http://dx.doi.org/10.3390/s21248222.

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The aim of the present work was to determine the main quality parameters on tuber potato using a portable near-infrared spectroscopy device (MicroNIR). Potato tubers protected by the Protected Geographical Indication (PGI “Patata de Galicia”, Spain) were analyzed both using chemical methods of reference and also using the NIR methodology for the determination of important parameters for tuber commercialization, such as dry matter and reducing sugars. MicroNIR technology allows for the attainment/estimation of dry matter and reducing sugars in the warehouses by directly measuring the tubers without a chemical treatment and destruction of samples. The principal component analysis and modified partial least squares regression method were used to develop the NIR calibration model. The best determination coefficients obtained for dry matter and reducing sugars were of 0.72 and 0.55, respectively, and with acceptable standard errors of cross-validation. Near-infrared spectroscopy was established as an effective tool to obtain prediction equations of these potato quality parameters. At the same time, the efficiency of portable devices for taking instantaneous measurements of crucial quality parameters is useful for potato processors.

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Ortiz-Aguayo, Dionisia, Marta Bonet-San-Emeterio, and Manel del Valle. "Simultaneous Voltammetric Determination of Acetaminophen, Ascorbic Acid and Uric Acid by Use of Integrated Array of Screen-Printed Electrodes and Chemometric Tools." Sensors 19, no. 15 (July 26, 2019): 3286. http://dx.doi.org/10.3390/s19153286.

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In the present work, ternary mixtures of Acetaminophen, Ascorbic acid and Uric acid were resolved using the Electronic tongue (ET) principle and Cyclic voltammetry (CV) technique. The screen-printed integrated electrode array having differentiated response for the three oxidizable compounds was formed by Graphite, Prussian blue (PB), Cobalt (II) phthalocyanine (CoPc) and Copper oxide (II) (CuO) ink-modified carbon electrodes. A set of samples, ranging from 0 to 500 µmol·L−1, was prepared, using a tilted (33) factorial design in order to build the quantitative response model. Subsequently, the model performance was evaluated with an external subset of samples defined randomly along the experimental domain. Partial Least Squares Regression (PLS) was employed to construct the quantitative model. Finally, the model successfully predicted the concentration of the three compounds with a normalized root mean square error (NRMSE) of 1.00 and 0.99 for the training and test subsets, respectively, and R2 ≥ 0.762 for the obtained vs. expected comparison graphs. In this way, a screen-printed integrated electrode platform can be successfully used for voltammetric ET applications.

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

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