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Qiang Zhang, Minji Li, Beibei Zhou, Junke Zhang, and Qinping Wei

analysis methods used in studies. In the present study, the partial least-squares regression was used to select variables based on VIP values, thus eliminating the interference of human factors and the complex collinearity among multiple independent

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Yohei Kurata, Tomoe Tsuchida, and Satoru Tsuchikawa

, principle component regression, and partial least squares regression (PLSR) analysis. It was possible to predict both the sugar and acid contents in apple with high precision using TOF-NIRS. In our previous paper ( Kurata and Tsuchikawa, 2009 ), a Q

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Yung-Kun Chuang, I-Chang Yang, Chao-Yin Tsai, Jiunn-Yan Hou, Yung-Huei Chang, and Suming Chen

the input for spectral analysis. Two standard multivariate analysis methods, modified partial least-squares regression (MPLSR) and stepwise multiple linear regression (SMLR), were used to explore the relationships between the reflectance spectra and

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Jian-rong Feng, Wan-peng Xi, Wen-hui Li, Hai-nan Liu, Xiao-fang Liu, and Xiao-yan Lu

the quality of these fruit to facilitate breeding of new cultivars meeting the consumer’s expectations. Fig. 2. Loading weight plot of principal components, PC1 vs. PC2 from a partial least squares regression (PLSR) model of volatile compounds, quality

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Stephen R. Delwiche, Weena Mekwatanakarn, and Chien Y. Wang

A rapid, reliable, and nondestructive method for quality evaluation of mango (Magnifera indica) fruit is important to the mango industry for international trade. The objective of this study was to determine the potential of near-infrared (NIR) spectroscopy to predict soluble solids content (SSC) and individual and combined concentrations of sucrose, glucose, and fructose nondestructively in mango. Mature mangoes at two different temperatures (15 °C and 20 °C) were measured by NIR interactance (750–1088 nm wavelength region analyzed) over an 11-day period, starting when the fruit were underripe and extending to a few days past optimal ripeness. Partial least squares regression was used to develop models for SSC, individual sugar concentration, and the sum of the concentrations of the three sugars. Such analyses yielded calibration equations with R 2 = 0.77 to 0.88 (SSC), 0.75 (sucrose), 0.67 (glucose), 0.70 (fructose), and 0.82 (sum); standard error of calibration = 0.56 to 0.90 (SSC), 10.0 (sucrose), 0.9 (glucose), 4.5 (fructose), and 10.4 (sum); and standard error of cross-validation = 0.93 to 1.10 (SSC), 15.6 (sucrose), 1.4 (glucose), 6.9 (fructose), and 16.8 (sum). When the SSC calibration was applied to a separate validation set, the standard error of performance ranged from 0.94% to 1.72%. These results suggest that for assessment of mango ripeness, NIR SSC calibrations are superior to the NIR calibrations for any of the individual sugars. This nondestructive technology can be used in the screening and grading of mangoes and in quality evaluation at wholesale and retail levels.

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David Jespersen and Brian Schwartz

included correlation analysis and partial least-squares regression. Partial least-squares regression was performed using the nonlinear iterative partial least squares method, and three factors were used based on the results of the van der Voet test. Traits

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Anne Plotto, Elizabeth Baldwin, Jinhe Bai, John Manthey, Smita Raithore, Sophie Deterre, Wei Zhao, Cecilia do Nascimento Nunes, Philip A. Stansly, and James A. Tansey

2013. Attributes preceded by the letter F and A stand for “Flavor” and “Aftertaste,” respectively. SSC = soluble solids content, TA = titratable acidity, TS = total sugars, L + N = limonin + nomilin. Fig. 5. Partial least square regressions biplot

Open access

Sai Xu, Huazhong Lu, and Xiuxiu Sun

the number of variables explored. The class to which the sample is assigned is that of the samples in the training group closest to it. Only the objects closest to K are used to make the assignments. The partial least-squares regression (PLSR) method

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Tyler Simons, Hanne Sivertsen, and Jean-Xavier Guinard

. The preferences of the second and third adult clusters were driven by juiciness, orange flavor, and overall flavor. The fourth cluster preferred samples higher in TA. Fig. 5. Partial least squares regression of consumer liking clusters for adults

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Jinwook Lee, In-Kyu Kang, Jacqueline F. Nock, and Christopher B. Watkins

by preharvest 1-MCP treatment alone. Fig. 2. Partial least squares regression scores (A) and loading (B) plots of models containing X-variables (fruit quality attributes and incidence of physiological disorders) and Y-variables [experimental factors