Model Population Analysis for Statistical Model Comparison

Li, Hongdong; Liang, Yi‐Zeng; Xu, Qingsong

doi:10.5772/31561

Cited by 1 publication

(2 citation statements)

References 44 publications

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“…29 Values significantly lower than 0.5 indicate the method is not reliable. 27,28 The AUC value obtained was 0.9839, which is very close to one. Thus, this indicates that the method was successful in classification of milk samples according to heat treatment.…”

Section: Discrimination According To Heat Treatment By Using Pls-dasupporting

confidence: 60%

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Discrimination of milk species using Raman spectroscopy coupled with partial least squares discriminant analysis in raw and pasteurized milk

et al. 2020

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BACKGROUND Heat treatment is the most common practice for the microbiological safety of milk; hence, determination of the heat treatment of milk is essential. Also, mislabeling or adulteration of expensive milk samples, like ewe or goat milk, with cow's milk is a growing problem in the dairy market. Thus, the determination of the authenticity of milk samples has crucial importance for both producers and consumers. The aim of this study was to discriminate milk samples using Raman spectroscopy with partial least squares discriminant analysis (PLS‐DA), first with regard to whether the milk was heat‐treated or not, and second with regard to species (cow, goat, ewe, mixture (adulterated)) in both raw and pasteurized milk. RESULTS First, discrimination of milk samples as raw or pasteurized was achieved using PLS‐DA. Both in calibration and prediction models, high sensitivity and specificity values were obtained for raw and pasteurized milk samples. Second, the proposed method also discriminated milk samples according to their species (cow, goat, ewe, and mixture) for both raw and pasteurized milk. In both calibration and prediction models, the sensitivity and specificity values were above 0.857 and 0.897 respectively. Also, the accuracy values were above 0.915. The results obtained denote satisfactory accurate classification of the samples. CONCLUSION The results suggest that Raman spectroscopy coupled with PLS‐DA can be successfully used to discriminate milk samples according to heat treatment (raw/pasteurized) and their species within 20 s per sample. It was seen that Raman spectra provide valuable information to be used especially for discrimination of milk samples according to their origin. © 2020 Society of Chemical Industry

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Section: Discrimination According To Heat Treatment By Using Pls-dasupporting

confidence: 60%

“…If AUC = 1 (or close to it), this indicates that the method is successful in identifying samples. 27,28 If this value is 0.5, this means there is a completely random selection among the classes. 29 Values significantly lower than 0.5 indicate the method is not reliable.…”

Section: Discrimination According To Heat Treatment By Using Pls-damentioning

confidence: 99%