Determination of Tibetan tea quality by hyperspectral imaging technology and multivariate analysis

Hu, Yan; Huang, Peng; Wang, Yuchao; Sun, Jie; Wu, Youli; Kang, Zhiliang

doi:10.1016/j.jfca.2023.105136

Cited by 11 publications

(4 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A high R 2 value and low RMSEC and RMSEP values indicate that the model has a strong predictive ability. − A large RPD value reflects the reliability of the model; a reliable model can accurately determine new unknown samples. RPD values < 1.4, between 1.4 and 2.0, and > 2 indicate that the constructed model is unreliable, relatively reliable, and has a high degree of reliability, respectively. − …”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Simultaneous Rapid Detection of Multiple Physicochemical Properties of Jet Fuel Using Near-Infrared Spectroscopy

Li,

Zhang,

Zhang

et al. 2024

ACS Omega

View full text Add to dashboard Cite

Jet fuel is the primary fuel used in the aviation industry, and its quality has a direct impact on the safety and operational efficiency of aircraft. The accurate quantitative detection and analysis of various physicochemical property indicators are important for improving and ensuring the quality of jet fuel in the domestic market. This study used near-infrared (NIR) spectroscopy to establish a suitable model for the simultaneous and rapid detection of multiple physicochemical properties in jet fuel. Using more than 40 different sources of jet fuel, a rapid detection model was established by optimizing the spectral processing methods. The measurement models were separately built using the partial least-squares (PLS) and orthogonal PLS algorithms, and the model parameters were optimized. The results show that after the Savitzky−Golay second derivative preprocessing, the PLS model built using the feature spectra selected by the uninformative variable elimination wavelength algorithm achieved the best measurement performance. Compared with the PLS model without preprocessing, the range of the resulting accuracy improvement was at least 15.01%. Under the optimal model parameters, the calibration set regression coefficient (R c 2 ) of the 11 jet fuel property index models ranged from 0.9102 to 0.9763, with the root-mean-square error of calibration values up to 0.8468 °C (for flash points). The regression coefficient (R p 2 ) of the validation set ranged from 0.8239 to 0.9557, with the root-mean-square error of prediction values up to 1.1354 °C (for flash points). The ratios of prediction to deviation (RPD) values were all in the range of 1.9−3.0, indicating high accuracy and reliability of the model. The rapid NIR analysis method established in this study enables the simultaneous and rapid detection of multiple physicochemical properties of jet fuel, thereby providing effective technical support for ensuring the quality of jet fuel in the market.

show abstract

Section: Methodsmentioning

confidence: 99%

“…RPD values < 1.4, between 1.4 and 2.0, and > 2 indicate that the constructed model is unreliable, relatively reliable, and has a high degree of reliability, respectively. 38 − 40 …”

Section: Methodsmentioning

confidence: 99%

Simultaneous Rapid Detection of Multiple Physicochemical Properties of Jet Fuel Using Near-Infrared Spectroscopy

Li,

Zhang,

Zhang

et al. 2024

ACS Omega

View full text Add to dashboard Cite

show abstract

“…For example, Liu et al examined the excellence and aging life of Pu-erh tea by HPLC fingerprint [ 11 ]. Tibetan tea grades could be classified by hyperspectral image and support vector machine [ 12 ]. Xu et al tried an electronic nose and computer vision for the evaluation of the fragrance and visual characteristics of tea leaves to determine the tea grades [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Near-infrared spectroscopy combined with fuzzy fast pseudoinverse linear discriminant analysis to discriminate mee tea grades

Wu,

Tang,

Zhou

et al. 2024

Heliyon

View full text Add to dashboard Cite

“…In recent years, HSI has been widely used for internal quality inspections of agricultural products. Hu et al [15] used HSI to determine levels of tea polyphenols and free amino acids by combining multiple preprocessing methods with machine learning (ML), and an analysis of their results showed that SG-SNV-PCA-Extratree's prediction accuracy ( R 2 P for tea polyphenols was 0.9248, and the precision (R 2 P of SG-MSC-PCA-Extratree's prediction of free amino acids was 0.8736. Wang et al [16] took a maize single seed as their research object, acquired hyperspectral images of it in the wavelength range of 930-2548 nm, proposed a combination of competitive adaptive reweighted sampling (CARS) and the successive projections algorithm (SPA), and extracted the feature bands, which were modelled using the PLSR and least squares support vector machine (LS-SVM) methods, respectively.…”

Section: Introductionmentioning

confidence: 99%

Rapid pH Value Detection in Secondary Fermentation of Maize Silage Using Hyperspectral Imaging

Yu,

Tian,

Zhao

et al. 2024

Agronomy

View full text Add to dashboard Cite

As pH is a key factor affecting the quality of maize silage, its accurate detection is essential to ensuring product quality. Although traditional methods for testing the pH of maize silage feed are widely used, the procedures are often complex and time-consuming and may damage the sample. This study presents a non-destructive hyperspectral imaging (HSI) technology that provides a more efficient and cost-effective method of monitoring pH by capturing the spectral information of samples and analyzing their chemical and physical properties rapidly and without contact. We applied four spectral preprocessing methods, among which the multiplicative scatter correction (MSC) preprocessing method yielded the best results. To minimize model redundancy and enhance predictive performance, we utilized six feature extraction methods for characteristic wavelength extraction, integrating these with partial least squares (PLS), non-linear support vector machine regression (SVR), and extreme learning machine (ELM) algorithms to construct a quantitative pH value prediction model. The results showed that the model based on the bootstrapping soft shrinkage (BOSS) feature wavelength extraction method outperformed the other feature extraction methods, selecting 20 pH value-related feature wavelengths from 256 bands and building a stable BOSS–ELM model with prediction set determination coefficient (RP2), root-mean-square error of prediction (RMSEP), and relative percentage deviation (RPD) values of 0.9241, 0.4372, and 3.6565, respectively. To further optimize the model for precisely predicting pH at each pixel in hyperspectral images, we employed three algorithms: the genetic algorithm (GA), whale optimization algorithm (WOA), and bald eagle search (BES). These algorithms optimized and compared the BOSS–ELM model to obtain the best model for predicting maize silage pH: the BOSS–BES–ELM model. This model achieved a determination coefficient (RP2) of 0.9598, an RMSEP of 0.3216, and an RPD of 5.1448. We generated a visualized distribution map of pH value variation in maize silage using the BOSS–BES–ELM model. This study provides strong technical support and a reference for the rapid, non-destructive detection of maize silage pH from an image, an advancement of great significance to ensuring the quality of maize silage.

show abstract

Determination of Tibetan tea quality by hyperspectral imaging technology and multivariate analysis

Cited by 11 publications

References 38 publications

Simultaneous Rapid Detection of Multiple Physicochemical Properties of Jet Fuel Using Near-Infrared Spectroscopy

Simultaneous Rapid Detection of Multiple Physicochemical Properties of Jet Fuel Using Near-Infrared Spectroscopy

Near-infrared spectroscopy combined with fuzzy fast pseudoinverse linear discriminant analysis to discriminate mee tea grades

Rapid pH Value Detection in Secondary Fermentation of Maize Silage Using Hyperspectral Imaging

Contact Info

Product

Resources

About