| Nondestructive Analysis of Internal Quality in Pears with a Self-Made Near-Infrared Spectrum Detector Combined with Multivariate Data Processing | |
| Wu, Xin1,2; Li, Guanglin1; He, Fengyun1 | |
| 2021-06 | |
| 发表期刊 | FOODS
 |
| EISSN | 2304-8158 |
| 卷号 | 10期号:6 |
| 摘要 | The consumption of pears has increased, thanks not only to their delicious and juicy flavor, but also their rich nutritional value. Traditional methods of detecting internal qualities (e.g., soluble solid content (SSC), titratable acidity (TA), and taste index (TI)) of pears are reliable, but they are destructive, time-consuming, and polluting. It is necessary to detect internal qualities of pears rapidly and nondestructively by using near-infrared (NIR) spectroscopy. In this study, we used a self-made NIR spectrum detector with an improved variable selection algorithm, named the variable stability and cluster analysis algorithm (VSCAA), to establish a partial least squares regression (PLSR) model to detect SSC content in snow pears. VSCAA is a variable selection method based on the combination of variable stability and cluster analysis to select the infrared spectrum variables. To reflect the advantages of VSCAA, we compared the classical variable selection methods (synergy interval partial least squares (SiPLS), genetic algorithm (GA), successive projections algorithm (SPA), and bootstrapping soft shrinkage (BOSS)) to extract useful wavelengths. The PLSR model, based on the useful variables selected by SiPLS-VSCAA, was optimal for measuring SSC in pears, and the correlation coefficient of calibration (Rc), root mean square error of cross validation (RMSECV), correlation coefficient of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) were 0.942, 0.198%, 0.936, 0.222%, and 2.857, respectively. Then, we applied these variable selection methods to select the characteristic wavelengths for measuring the TA content and TI value in snow pears. The prediction PLSR models, based on the variables selected by GA-BOSS to measure TA and that by GA-VSCAA to detect TI, were the best models, and the Rc, RMSECV, Rp and RPD were 0.931, 0.124%, 0.912, 0.151%, and 2.434 and 0.968, 0.080%, 0.968, 0.089%, and 3.775, respectively. The results showed that the self-made NIR-spectrum detector based on a portable NIR spectrometer with multivariate data processing was a good tool for rapid and nondestructive analysis of internal quality in pears. |
| 关键词 | variable selection methods variable stability and cluster analysis algorithm (VSCAA) internal quality near-infrared (NIR) spectroscopy pears |
| DOI | 10.3390/foods10061315 |
| 收录类别 | SCIE |
| 语种 | 英语 |
| WOS研究方向 | Food Science & Technology |
| WOS类目 | Food Science & Technology |
| WOS记录号 | WOS:000665982500001 |
| 出版者 | MDPI |
| 原始文献类型 | Article |
| 出版地 | BASEL |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | https://ir.cqcet.edu.cn/handle/39TD4454/3563 |
| 专题 | 重庆电子科技职业大学 |
| 作者单位 | 1.Southwest Univ, Coll Engn & Technol, Dept Agr Engn, Chongqing 400715, Peoples R China; 2.Chongqing Coll Elect Engn, Dept Elect & Internet Things, Chongqing 401331, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wu, Xin,Li, Guanglin,He, Fengyun. Nondestructive Analysis of Internal Quality in Pears with a Self-Made Near-Infrared Spectrum Detector Combined with Multivariate Data Processing[J]. FOODS,2021,10(6). |
| APA | Wu, Xin,Li, Guanglin,&He, Fengyun.(2021).Nondestructive Analysis of Internal Quality in Pears with a Self-Made Near-Infrared Spectrum Detector Combined with Multivariate Data Processing.FOODS,10(6). |
| MLA | Wu, Xin,et al."Nondestructive Analysis of Internal Quality in Pears with a Self-Made Near-Infrared Spectrum Detector Combined with Multivariate Data Processing".FOODS 10.6(2021). |
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