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Nondestructive Detection of Weight Loss Rate, Surface Color, Vitamin C Content, and Firmness in Mini-Chinese Cabbage with Nanopackaging by Fourier Transform-Near Infrared Spectroscopy

文献类型： 外文期刊

作者： Liu, Qiang 1 ; Chen, Shaoxia 1 ; Zhou, Dandan 3 ; Ding, Chao 2 ; Wang, Jiahong 3 ; Zhou, Hongsheng 4 ; Tu, Kang 1 ; Pan, 1 ;

作者机构： 1.Nanjing Agr Univ, Coll Food Sci & Technol, Nanjing 210095, Peoples R China

2.Nanjing Univ Finance & Econ, Coll Food Sci & Engn, Nanjing 210023, Peoples R China

3.Nanjing Forestry Univ, Coll Light Ind & Food Engn, Nanjing 210037, Peoples R China

4.Jiangsu Acad Agr Sci, Inst Agr Facil & Equipment, Nanjing 210014, Peoples R China

关键词： near infrared spectroscopy; nondestructive detection; mini-Chinese cabbage; nanopacking; storage

期刊名称：FOODS （ 影响因子：4.35； 五年影响因子：4.957 ）

ISSN：

年卷期： 2021 年 10 卷 10 期

页码：

收录情况： SCI

摘要： A nondestructive optical method is described for the quality assessment of mini-Chinese cabbage with nanopackaging during its storage, using Fourier transform-near infrared (FT-NIR) spectroscopy. The sample quality attributes measured included weight loss rate, surface color index, vitamin C content, and firmness. The level of freshness of the mini-Chinese cabbage during storage was divided into three categories. Partial least squares regression (PLSR) and the least squares support vector machine were applied to spectral datasets in order to develop prediction models for each quality attribute. For a comparative analysis of performance, the five preprocessing methods applied were standard normal variable (SNV), first derivative (lst), second derivative (2nd), multiplicative scattering correction (MSC), and auto scale. The SNV-PLSR model exhibited the best prediction performance for weight loss rate (R-p(2) = 0.96, RMSEP = 1.432%). The 1st-PLSR model showed the best prediction performance for L* value (R-p(2) = 0.89, RMSEP = 3.25 mg/100 g), but also the lowest accuracy for firmness (R-p(2) = 0.60, RMSEP = 2.453). The best classification model was able to predict freshness levels with 88.8% accuracy in mini-Chinese cabbage by supported vector classification (SVC). This study illustrates that the spectral profile obtained by FT-NIR spectroscopy could potentially be implemented for integral assessments of the internal and external quality attributes of mini-Chinese cabbage with nanopacking during storage.

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