Nondestructive quality assessment of chili peppers using near-infrared hyperspectral imaging combined with multivariate analysis

Jiang, Jin-Lin; Cen, Haiyan; Zhang, Chu; Lyu, Xiaohan; Weng, Haiyong; Xu, Haixia; He, Yong

doi:10.1016/j.postharvbio.2018.09.003

Cited by 51 publications

(24 citation statements)

References 41 publications

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“…Wavelengths selection in multivariate analysis is very essential as it can reduce the analyzed wavelengths to shorten the time required for the prediction process (Jiang et al, ). In this study, UVE, CARS, SPA, RC, and the combination of wavelengths selection methods were applied to choose the wavelengths related to the total phenolic content of Flos Lonicerae from full spectrum, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…By hyperspectral data analysis, the external and internal characteristics of the samples can be predicted. Previous studies have convincingly demonstrated that HSI technology possesses the potential for assessing capsaicin concentrations and water content in chili peppers (Jiang et al, 2018), anthocyanin content in sweet potato , protein content in wheat kernels (Caporaso, Whitworth, & Fisk, 2018), sugar content in dangshang pear , decayed honey peaches , firmness and sweetness of tomatoes (Rahman, Park, Bae, & Cho, 2018), moisture content of cooked beef (Yang et al, 2017), total pigments of red meats (Xiong et al, 2015), and so on. However, the research on determining total phenolic content in Flos Lonicerae by using HSI technology is scarce.…”

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confidence: 99%

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Total phenolic content prediction inFlos Loniceraeusing hyperspectral imaging combined with wavelengths selection methods

Liu

Wang

Gao

et al. 2019

J Food Process Engineering

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Hyperspectral imaging (HSI) method was applied to rapidly and nondestructively predict total phenolic content in Flos Lonicerae. The least squares support vector machine (LS‐SVM) and partial least squares regression (PLSR) models were developed on the basis of full wavelengths data and characteristic wavelengths data chosen by six wavelengths selection ways. The results clarified that standard normal variable (SNV) was the optimal pretreatment method, and the nonlinear LS‐SVM model based on the characteristic wavelengths chosen by the combination (CARS‐SPA) of competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) yielded the best prediction performance for the total phenolic content. The overall results demonstrated that the proposed CARS‐SPA was a good method for selecting characteristic wavelengths to enhance prediction performance of HSI, and nonlinear LS‐SVM was more appropriate than linear PLSR for the prediction of total phenolic content in Flos Lonicerae. Practical applications Flos Lonicerae is a well‐known traditional Chinese medicinal herb, and also the raw material of various medicines and herbal tea. The demand for Flos Lonicerae is increasing. Therefore, monitoring the quality of Flos Lonicerae is very important to consumers and industry of Flos Lonicerae. The phenolic compounds content in Flos Lonicerae is one of the key internal quality factors. The traditional methods (such as spectrophotometric techniques or high‐performance liquid chromatography) for detecting total phenolic content are time‐consuming, labor‐consuming, and destructive. Hyperspectral imaging (HSI) is a nondestructive and reliable method for quality inspection. The results showed that SNV was determined as the best method for pretreatment, and the nonlinear CARS‐SPA‐LS‐SVM model was developed for total phenolic content detection with high precision. It could be verified that HSI technology is effective and promising for rapid determination of total phenolic content and quality control of Flos Lonicerae as well as other agricultural products.

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Total phenolic content prediction inFlos Loniceraeusing hyperspectral imaging combined with wavelengths selection methods

Liu

Wang

Gao

et al. 2019

J Food Process Engineering

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“…In this study, four supervised classifiers including the extreme learning machine (ELM), K-nearest neighbor algorithm (KNN), linear discriminant analysis (LDA), naïve Bayes classifier (NB) and the support vector machine (SVM) were introduced. ELM is a feedforward network with a single hidden layer carrying the performance of fast learning speed and good generalization and was widely used for pattern recognition [26,27]. The classification ability largely depends on the number of hidden nodes.…”

Section: Discriminant Analysis Methods For Disease Detectionmentioning

confidence: 99%

“…The procedure of reducing the data size through optimal wavelengths selection is generally considered important in HSI data analysis for a more robust classification model with a high computing efficiency [27,36]. Figure 3 shows the optimal wavelengths selected by RF, SPA and SFS.…”

Section: Optimal Wavelengths Selection For Disease Detection Of a Thmentioning

confidence: 99%

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Hyperspectral Reflectance Imaging Combined with Multivariate Analysis for Diagnosis of Sclerotinia Stem Rot on Arabidopsis Thaliana Leaves

et al. 2019

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Sclerotinia stem rot (SSR) is one of the most destructive diseases in the world caused by Sclerotinia sclerotiorum (S. sclerotiorum), resulting in significant yield loss. Early and high-throughput detection would be critical to prevent SSR from spreading. This study aimed to propose a feasible method for SSR detection based on the hyperspectral imaging coupled with multivariate analysis. The performance of different detecting algorithms were compared by combining the extreme learning machine (ELM), K-nearest neighbor algorithm (KNN), linear discriminant analysis (LDA), naïve Bayes classifier (NB) and the support vector machine (SVM) with the random frog (RF), successive projection algorithm (SPA) and sequential forward selection (SFS). The similarity of selected optimal wavelengths by three different feature selection methods indicated a high correlation between selected wavelengths and SSR. Compared with KNN, LDA, NB, and SVM, three wavelengths (455, 671 and 747 nm) selected by SFS-CA combined with ELM could achieve relatively better results with the overall accuracy of 93.7% and the lowest false negative rate of 2.4%. These results demonstrated the potential of the presented method using hyperspectral reflectance imaging combined with multivariate analysis for SSR diagnosis.

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Rapid and comprehensive quality evaluation of Huang‐qin from different origins by FT‐IR and NIR spectroscopy combined with chemometrics

Lv,

Yin,

et al. 2024

Phytochemical Analysis

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IntroductionQuality evaluation of Huang‐qin is significant to ensure its clinical efficacy.ObjectiveThis study aims to establish an accurate, rapid and comprehensive Huang‐qin quality evaluation method to overcome the time‐consuming and laborious shortcomings of traditional herbal medicine quality assessment methods.MethodsThe contents of baicalin, baicalein and scutellarin in Huang‐qin from five different origins were analyzed by FT‐IR and NIR spectra combined with multivariate data technology. The quality of Huang‐qin from different origins was evaluated by TOPSIS and consistency analysis based on the content of three active ingredients. The correlation between ecological factors and the accumulation of active ingredients was explored.ResultsSatisfactory prediction results of PLS models were obtained. Relatively, the model based on FT‐IR combined with the PLS regression method has higher R2 and smaller RMSE than the NIR combined with the PLS method. TOPSIS and consistency analysis results showed that the quality of Huang‐qin from different geographical origins was significantly different. The results showed that the quality of Huang‐qin produced in Shanxi Province was the best among the five origins studied. The results also found that the quality of Huang‐qin in different growing areas of the same origin was not completely consistent. The correlation study showed that altitude, sunshine duration and rainfall were the main factors that caused the quality difference of medicinal materials in different geographical origins.ConclusionThis study provides a reference for the rapid quantitative analysis of the active components of herbal medicine and the quality evaluation of them.

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Nondestructive quality assessment of chili peppers using near-infrared hyperspectral imaging combined with multivariate analysis

Cited by 51 publications

References 41 publications

Total phenolic content prediction inFlos Loniceraeusing hyperspectral imaging combined with wavelengths selection methods

Total phenolic content prediction inFlos Loniceraeusing hyperspectral imaging combined with wavelengths selection methods

Hyperspectral Reflectance Imaging Combined with Multivariate Analysis for Diagnosis of Sclerotinia Stem Rot on Arabidopsis Thaliana Leaves

Rapid and comprehensive quality evaluation of Huang‐qin from different origins by FT‐IR and NIR spectroscopy combined with chemometrics

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