利用电子眼和近红外光谱结合化学计量学方法快速评价炒制白芍的质量

IF 4 2区农林科学 Q2 CHEMISTRY, APPLIED

Journal of Food Composition and Analysis Pub Date : 2025-04-10 DOI:10.1016/j.jfca.2025.107621

Yatong Kang , Tingze Long , Ying Qiao , Han Yi , Feng Wang , Chao Chen

{"title":"利用电子眼和近红外光谱结合化学计量学方法快速评价炒制白芍的质量","authors":"Yatong Kang , Tingze Long , Ying Qiao , Han Yi , Feng Wang , Chao Chen","doi":"10.1016/j.jfca.2025.107621","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><div>Electronic eye (E-eye) and Near-infrared spectroscopy (NIRS) in conjunction with chemometric methods were investigated for rapid quality assessment of fried Radix Paeoniae Alba (FRPA) samples across various processing degrees and accurate quantification of their chemical components.</div></div><div><h3>Method</h3><div>Raw samples were collected and subjected to stir-frying, with variations in processing time resulting in FRPA samples characterized by different processing degrees. After acquiring E-eye and NIRS data, principal component analysis (PCA) and K-nearest neighbors (KNN) were used to build the qualitative models. Meanwhile, the NIRS data were correlated with the content of four chemical components (<em>i.e.</em>, gallic acid, albiflorin, paeoniflorin, and total phenols) in FRPA samples. The partial least squares regression (PLSR) algorithm was employed to establish the quantitative models, which were optimized through the selection of appropriate spectra preprocessing methods and wavelength selection techniques. The evaluation metrics include the coefficient of determination (<em>R²</em>), root mean square error (<em>RMSE</em>), and residual prediction deviation (<em>RPD</em>) for both calibration and prediction.</div></div><div><h3>Results</h3><div>In PCA analysis, the samples with different stir-frying times can show a certain distribution trend, but the samples with adjacent stir-frying times overlap each other. However, in KNN modeling, the samples can be classified with 100 % accuracy, regardless of whether E-eye data or NIRS spectra were used. The most efficacious quantitative models were attained by implementing the first derivative preprocessing and Jaya wavelength screening methods. These models exhibited <em>RPD</em> higher than 3, with calibrated and predicted <em>R²</em> values exceeding 0.95.</div></div><div><h3>Conclusion</h3><div>The present models exhibit significant efficacy and provide a valuable tool for rapid quality assessment of FRPA samples.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"143 ","pages":"Article 107621"},"PeriodicalIF":4.0000,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Rapid quality evaluation of fried Radix Paeoniae Alba (Paeonia lactiflora Pall.) using electronic eye and near-infrared spectroscopy combined with chemometric methods\",\"authors\":\"Yatong Kang , Tingze Long , Ying Qiao , Han Yi , Feng Wang , Chao Chen\",\"doi\":\"10.1016/j.jfca.2025.107621\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objective</h3><div>Electronic eye (E-eye) and Near-infrared spectroscopy (NIRS) in conjunction with chemometric methods were investigated for rapid quality assessment of fried Radix Paeoniae Alba (FRPA) samples across various processing degrees and accurate quantification of their chemical components.</div></div><div><h3>Method</h3><div>Raw samples were collected and subjected to stir-frying, with variations in processing time resulting in FRPA samples characterized by different processing degrees. After acquiring E-eye and NIRS data, principal component analysis (PCA) and K-nearest neighbors (KNN) were used to build the qualitative models. Meanwhile, the NIRS data were correlated with the content of four chemical components (<em>i.e.</em>, gallic acid, albiflorin, paeoniflorin, and total phenols) in FRPA samples. The partial least squares regression (PLSR) algorithm was employed to establish the quantitative models, which were optimized through the selection of appropriate spectra preprocessing methods and wavelength selection techniques. The evaluation metrics include the coefficient of determination (<em>R²</em>), root mean square error (<em>RMSE</em>), and residual prediction deviation (<em>RPD</em>) for both calibration and prediction.</div></div><div><h3>Results</h3><div>In PCA analysis, the samples with different stir-frying times can show a certain distribution trend, but the samples with adjacent stir-frying times overlap each other. However, in KNN modeling, the samples can be classified with 100 % accuracy, regardless of whether E-eye data or NIRS spectra were used. The most efficacious quantitative models were attained by implementing the first derivative preprocessing and Jaya wavelength screening methods. These models exhibited <em>RPD</em> higher than 3, with calibrated and predicted <em>R²</em> values exceeding 0.95.</div></div><div><h3>Conclusion</h3><div>The present models exhibit significant efficacy and provide a valuable tool for rapid quality assessment of FRPA samples.</div></div>\",\"PeriodicalId\":15867,\"journal\":{\"name\":\"Journal of Food Composition and Analysis\",\"volume\":\"143 \",\"pages\":\"Article 107621\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2025-04-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Food Composition and Analysis\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0889157525004363\",\"RegionNum\":2,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Food Composition and Analysis","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0889157525004363","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, APPLIED","Score":null,"Total":0}

引用次数: 0

摘要

目的利用电子眼（E-eye）和近红外光谱（NIRS）结合化学计量学方法，对不同加工程度的白芍煎炸样品进行快速质量评价，并对其化学成分进行准确定量。方法收集6个样品，通过不同的处理时间进行翻炒，得到处理程度不同的FRPA样品。在获取E-eye和NIRS数据后，利用主成分分析（PCA）和k近邻分析（KNN）建立定性模型。同时，NIRS数据与FRPA样品中四种化学成分（没食子酸、芍药苷、芍药苷和总酚）的含量进行了相关性分析。采用偏最小二乘回归（PLSR）算法建立定量模型，并通过选择合适的光谱预处理方法和波长选择技术对模型进行优化。评估指标包括校正和预测的决定系数（R²）、均方根误差（RMSE）和残差预测偏差（RPD）。结果在主成分分析中，不同炒制次数的样品呈现一定的分布趋势，但相邻炒制次数的样品相互重叠。然而，在KNN建模中，无论使用E-eye数据还是NIRS光谱，样本的分类准确率都可以达到100 %。采用一阶导数预处理和Jaya波长筛选方法获得了最有效的定量模型。这些模型的RPD值均大于3，校正和预测的R²值均大于0.95。结论该模型具有显著的疗效，为快速评价FRPA样品的质量提供了一种有价值的工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Rapid quality evaluation of fried Radix Paeoniae Alba (Paeonia lactiflora Pall.) using electronic eye and near-infrared spectroscopy combined with chemometric methods

Objective

Electronic eye (E-eye) and Near-infrared spectroscopy (NIRS) in conjunction with chemometric methods were investigated for rapid quality assessment of fried Radix Paeoniae Alba (FRPA) samples across various processing degrees and accurate quantification of their chemical components.

Method

Raw samples were collected and subjected to stir-frying, with variations in processing time resulting in FRPA samples characterized by different processing degrees. After acquiring E-eye and NIRS data, principal component analysis (PCA) and K-nearest neighbors (KNN) were used to build the qualitative models. Meanwhile, the NIRS data were correlated with the content of four chemical components (i.e., gallic acid, albiflorin, paeoniflorin, and total phenols) in FRPA samples. The partial least squares regression (PLSR) algorithm was employed to establish the quantitative models, which were optimized through the selection of appropriate spectra preprocessing methods and wavelength selection techniques. The evaluation metrics include the coefficient of determination (R²), root mean square error (RMSE), and residual prediction deviation (RPD) for both calibration and prediction.

Results

In PCA analysis, the samples with different stir-frying times can show a certain distribution trend, but the samples with adjacent stir-frying times overlap each other. However, in KNN modeling, the samples can be classified with 100 % accuracy, regardless of whether E-eye data or NIRS spectra were used. The most efficacious quantitative models were attained by implementing the first derivative preprocessing and Jaya wavelength screening methods. These models exhibited RPD higher than 3, with calibrated and predicted R² values exceeding 0.95.

Conclusion

The present models exhibit significant efficacy and provide a valuable tool for rapid quality assessment of FRPA samples.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Journal of Food Composition and Analysis 工程技术-食品科技

CiteScore

6.20

自引率

11.60%

发文量

601

审稿时长

53 days

期刊介绍： The Journal of Food Composition and Analysis publishes manuscripts on scientific aspects of data on the chemical composition of human foods, with particular emphasis on actual data on composition of foods; analytical methods; studies on the manipulation, storage, distribution and use of food composition data; and studies on the statistics, use and distribution of such data and data systems. The Journal''s basis is nutrient composition, with increasing emphasis on bioactive non-nutrient and anti-nutrient components. Papers must provide sufficient description of the food samples, analytical methods, quality control procedures and statistical treatments of the data to permit the end users of the food composition data to evaluate the appropriateness of such data in their projects. The Journal does not publish papers on: microbiological compounds; sensory quality; aromatics/volatiles in food and wine; essential oils; organoleptic characteristics of food; physical properties; or clinical papers and pharmacology-related papers.