Background Tea is one of the most popular beverages around the world for its health benefit. The availability of rapid and reliable techniques for evaluating tea quality and safety is important for the tea industry. However, tea components and pesticide residues detection, sensory analysis, grading, and pricing are carried out by laborious, time-consuming, and destructive methods. Therefore, vibrational spectroscopy has emerged as an increasingly popular technology in tea quality and safety evaluation owing to its simple, nondestructive and rapid detection procedures. Scope and approach The current review compares the employment of different vibrational spectroscopic techniques including near-infrared (NIR), mid-infrared (MIR), Raman, Terahertz (THz) spectroscopies and hyperspectral imaging (HSI) in tandem with regression and classification models in the quantitative and qualitative analyses of tea quality and safety. Furthermore, band assignments of tea components based on these spectroscopies are summarized. Key findings and conclusions NIR spectroscopy dominates the applications of vibrational spectroscopy in the evaluation of tea quality, while only one study is related to THz spectroscopy. Raman technology is mainly applied to detect pesticide residues. HSI providing both spectral and spatial information has a potential in the optimization of the tea production process. Additionally, regression models find widespread use in the quantitative analysis of tea, such as phenolics, flavonoids, pigments, sensory evaluation, and pesticide residues, while classification models are useful for discriminating geographical origins, varieties, and fermentation degrees. The online detection of tea quality and safety by vibrational spectroscopy on the industrial scale is still an important and challenging task for future studies.

Recent developments in vibrational spectroscopic techniques for tea quality and safety analyses

A data fusion approach on confocal Raman microspectroscopy and electronic nose for quantitative evaluation of pesticide residue in tea

In this study, an electronic nose (E-nose) consisting of seven metal oxide semiconductor sensors is developed to identify milk sources (dairy farms) and to estimate the content of milk fat and protein which are the indicators of milk quality. The developed E-nose is a low cost and non-destructive device. For milk source identification, the features based on milk odor features from E-nose, composition features (Dairy Herd Improvement, DHI analytical data) from DHI analysis and fusion features are analyzed by principal component analysis (PCA) and linear discriminant analysis (LDA) for dimension reduction and then three machine learning algorithms, logistic regression (LR), support vector machine (SVM), and random forest (RF), are used to construct the classification model of milk source (dairy farm) identification. The results show that the SVM model based on the fusion features after LDA has the best performance with the accuracy of 95%. Estimation model of the content of milk fat and protein from E-nose features using gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and random forest (RF) are constructed. The results show that the RF models give the best performance (R2 = 0.9399 for milk fat; R2 = 0.9301 for milk protein) and indicate that the proposed method in this study can improve the estimation accuracy of milk fat and protein, which provides a technical basis for predicting the quality of milk.

https://www.mdpi.com/1424-8220/20/15/4238/pdf

Milk Source Identification and Milk Quality Estimation Using an Electronic Nose and Machine Learning Techniques

/pdf/electronic-sensor-technologies-in-monitoring-quality-of-tea-23t1mx6y.pdf

Electronic Sensor Technologies in Monitoring Quality of Tea: A Review

Soluble solids content (SSC) is one of the important components for evaluating fruit quality. The rapid development of hyperspectral imagery provides an efficient method for non-destructive detection of SSC. Previous studies have shown that the internal quality evaluation of fruits based on spectral information features achieves better results. However, the lack of comprehensive features limits the accurate estimation of fruit quality. Therefore, the deep learning theory is applied to the estimation of the soluble solid content of peaches, a method for estimating the SSC of fresh peaches based on the deep features of the hyperspectral image fusion information is proposed, and the estimation models of different neural network structures are designed based on the stack autoencoder–random forest (SAE-RF). The results show that the accuracy of the model based on the deep features of the fusion information of hyperspectral imagery is higher than that of the model based on spectral features or image features alone. In addition, the SAE-RF model based on the 1237-650-310-130 network structure has the best prediction effect (R2 = 0.9184, RMSE = 0.6693). Our research shows that the proposed method can improve the estimation accuracy of the soluble solid content of fresh peaches, which provides a theoretical basis for the non-destructive detection of other components of fresh peaches.

Estimation Method of Soluble Solid Content in Peach Based on Deep Features of Hyperspectral Imagery.

Tea polyphenols are important ingredients for evaluating tea quality. The rapid development of sensors provides an efficient method for nondestructive detection of tea polyphenols. Previous studies have shown that features obtained from single or multiple sensors yield better results in detecting interior tea quality. However, due to their lack of external features, it is difficult to meet the general evaluation model for the quality of the interior and exterior of tea. In addition, some features do not fully reflect the sensor signals of tea for several categories. Therefore, a feature fusion method based on time and frequency domains from electronic nose (E-nose) and hyperspectral imagery (HSI) is proposed to estimate the polyphenol content of tea for cross-category evaluation. The random forest and the gradient boosting decision tree (GBDT) are used to evaluate the feature importance to obtain the optimized features. Three models based on different features for cross-category tea (black tea, green tea, and yellow tea) were compared, including grid support vector regression (Grid-SVR), random forest (RF), and extreme gradient boosting (XGBoost). The results show that the accuracy of fusion features based on the time and frequency domain from the electronic nose and hyperspectral image system is higher than that of the features from single sensor. Whether based on all original features or optimized features, the performance of XGBoost is the best among the three regression algorithms (R2 = 0.998, RMSE = 0.434). Results indicate that the proposed method in this study can improve the estimation accuracy of tea polyphenol content for cross-category evaluation, which provides a technical basis for predicting other components of tea.

https://www.mdpi.com/1424-8220/20/1/50/pdf

Cross-Category Tea Polyphenols Evaluation Model Based on Feature Fusion of Electronic Nose and Hyperspectral Imagery.

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Cross-Category Tea Polyphenols Evaluation Model Based on Feature Fusion of Electronic Nose and Hyperspectral Imagery.