Fourier Transform Near-Infrared Spectroscopy and Chemometrics To Predict Zygosacchromyces rouxii in Apple and Kiwi Fruit Juices

作者：Niu, Chen; Guo, Hong; Wei, Jianping; Sajid, Marina; Yuan, Yahong; Yue, Tianli^*

来源：Journal of Food Protection, 2018, 81(8): 1379-1385.

DOI：10.4315/0362-028X.JFP-17-512

• Apple and kiwi fruit juices
• Near-infrared spectroscopy
• Support vector machine regression
• Zygosacchromyces rouxii

This study investigated the capability of near-infrared spectroscopy (MRS) to predict the concentration of Zygosaccharomyces rouxii in apple and kiwi fruit juices. The yeast was inoculated in fresh kiwi fruit juice (n = 68), reconstituted kiwi juice (n = 85), and reconstituted apple juice (n = 64), followed by NW spectra collection and plate counting. A principal component analysis indicated direct orthogonal signal correction preprocessing was suitable to separate spectral samples. Parameter optimization algorithms increased the performance of support vector machine regression models developed in a single variety juice system and a multiple variety juice system. Single variety juice models achieved accurate prediction of Z. rouxii concentrations, with the limit of quantification at 3 to 15 CFU/mL (R-2 = 0.997 to 0.999), and the method was also feasible for Hanseniaspora uvarum and Candida tropicalis. The best multiple variety juice model obtained had a limit of quantification of 237 CFU/mL (R-2 = 0.961) for Z. rouxii. A Bland-Altman analysis indicated good agreement between the support vector machine regression model and the plate counting method. It suggests that NIRS can be a high-throughput method for prediction of Z. rouxii counts in kiwi fruit and apple juices.

• 出版日期2018-8
• 单位西北农林科技大学