The Quality Control of Tea by Near-Infrared Reflectance (NIR) Spectroscopy and Chemometrics

Zhu, Mingzhi; Wen, Bin; Wu, Hao; Li, Juan; Lin, Haiyan; Li, Qin; Li, Yinhua; Huang, Jianan; Z, Liu

doi:10.1155/2019/8129648

Cited by 61 publications

(29 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Visible/near-infrared spectroscopy (VNIRS) is widely used as a rapid and non-destructive technique for tea classification [6][7][8] and tea composition determination [9][10][11]. Hyperspectral imaging (HSI) is a technique integrating both spectroscopic techniques and imaging techniques, acquiring spectral and spatial information simultaneously.…”

Section: Introductionmentioning

confidence: 99%

“…In general, a certain amount of tea samples (contains tens or more tea leaves in a sample) is used for VINRS and HSI analysis and overlap of tea leaves occurs in this situation [6][7][8][9][10][17][18][19]. Thus it is difficult to identify adulterations in this amount of tea when some amounts of adulterations are mixed into the normal sample.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Rapid and Nondestructive Discrimination of Geographical Origins of Longjing Tea using Hyperspectral Imaging at Two Spectral Ranges Coupled with Machine Learning Methods

Hong

2020

Applied Sciences

View full text Add to dashboard Cite

Longjing tea is one of China’s protected geographical indication products with high commercial and nutritional value. The geographical origin of Longjing tea is an important factor influencing its commercial and nutritional value. Hyperspectral imaging systems covering the two spectral ranges of 380–1030 nm and 874–1734 nm were used to identify a single tea leaf of Longjing tea from six geographical origins. Principal component analysis (PCA) was conducted on hyperspectral images to form PCA score images. Differences among samples from different geographical origins were visually observed from the PCA score images. Support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) models were built using the full spectra at the two spectral ranges. Decent classification performances were obtained at the two spectral ranges, with the overall classification accuracy of the calibration and prediction sets over 84%. Furthermore, prediction maps for geographical origins identification of Longjing tea were obtained by applying the SVM models on the hyperspectral images. The overall results illustrate that hyperspectral imaging at both spectral ranges can be applied to identify the geographical origin of single tea leaves of Longjing tea. This study provides a new, rapid, and non-destructive alternative for Longjing tea geographical origins identification.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rapid and Nondestructive Discrimination of Geographical Origins of Longjing Tea using Hyperspectral Imaging at Two Spectral Ranges Coupled with Machine Learning Methods

Hong

2020

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Importantly, the recently reported approach showed that recovery of parameters in a multi-wavelength time-resolved data can be achieved without prior instrument calibration [47]. Other notable NIR innovations in instrumental methods for spectral analysis during the review period includes a dual detection channel instrument for NIR time-resolved diffuse optical spectroscopy [48], functional NIR spectroscopy (fNIRS) instruments for high-density diffuse optical tomography [49,50], and other NIR-based sensors [51][52][53][54][55].…”

Section: Portable Nir Sensorsmentioning

confidence: 99%

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

Bwambok

Siraj

Macchi

et al. 2020

Sensors

View full text Add to dashboard Cite

Quality checks, assessments, and the assurance of food products, raw materials, and food ingredients is critically important to ensure the safeguard of foods of high quality for safety and public health. Nevertheless, quality checks, assessments, and the assurance of food products along distribution and supply chains is impacted by various challenges. For instance, the development of portable, sensitive, low-cost, and robust instrumentation that is capable of real-time, accurate, and sensitive analysis, quality checks, assessments, and the assurance of food products in the field and/or in the production line in a food manufacturing industry is a major technological and analytical challenge. Other significant challenges include analytical method development, method validation strategies, and the non-availability of reference materials and/or standards for emerging food contaminants. The simplicity, portability, non-invasive, non-destructive properties, and low-cost of NIR spectrometers, make them appealing and desirable instruments of choice for rapid quality checks, assessments and assurances of food products, raw materials, and ingredients. This review article surveys literature and examines current challenges and breakthroughs in quality checks and the assessment of a variety of food products, raw materials, and ingredients. Specifically, recent technological innovations and notable advances in quartz crystal microbalances (QCM), electroanalytical techniques, and near infrared (NIR) spectroscopic instrument development in the quality assessment of selected food products, and the analysis of food raw materials and ingredients for foodborne pathogen detection between January 2019 and July 2020 are highlighted. In addition, chemometric approaches and multivariate analyses of spectral data for NIR instrumental calibration and sample analyses for quality assessments and assurances of selected food products and electrochemical methods for foodborne pathogen detection are discussed. Moreover, this review provides insight into the future trajectory of innovative technological developments in QCM, electroanalytical techniques, NIR spectroscopy, and multivariate analyses relating to general applications for the quality assessment of food products.

show abstract

“…The tea plant [ Camellia sinensis (L.) O. Kuntze], cultivated for the production of non-alcoholic beverages, is an economically important woody crop worldwide ( Zhu et al, 2017 , 2019 , 2020b ; Yang et al, 2019 ). Leaf color is an important agronomic trait of this plant.…”

Section: Introductionmentioning

confidence: 99%

Metabolic Profiling and Gene Expression Analyses of Purple-Leaf Formation in Tea Cultivars (Camellia sinensis var. sinensis and var. assamica)

et al. 2021

Self Cite

View full text Add to dashboard Cite

Purple-leaf tea cultivars are known for their specific chemical composition that greatly influences tea bioactivity and plant resistance. Some studies have tried to reveal the purple-leaf formation mechanism of tea by comparing the purple new leaves and green older leaves in the same purple-leaf tea cultivar. It has been reported that almost all structural genes involved in anthocyanin/flavonoid biosynthesis were down-regulated in purple-leaf tea cultivars when the purple new leaves become green older leaves. However, anthocyanin/flavonoid biosynthesis is also affected by the growth period of tea leaves, gradually decreasing as new tea leaves become old tea leaves. This leads to uncertainty as to whether the purple-leaf formation is attributed to the high expression of structural genes in anthocyanin/flavonoid biosynthesis. To better understand the mechanisms underlying purple-leaf formation, we analyzed the biosynthesis of three pigments (chlorophylls, carotenoids, and anthocyanins/flavonoids) by integrated metabolic and gene expression analyses in four purple-leaf tea cultivars including Camellia sinensis var. sinensis and var. assamica. Green-leaf and yellow-leaf cultivars were employed for comparison. The purple-leaf phenotype was mainly attributed to high anthocyanins and low chlorophylls. The purple-leaf phenotype led to other flavonoid changes including lowered monomeric catechin derivatives and elevated polymerized catechin derivatives. Gene expression analysis revealed that 4-coumarate: CoA ligase (4CL), anthocyanidin synthase (ANS), and UDP-glucose: flavonoid 3-O-glucosyltransferase (UFGT) genes in the anthocyanin biosynthetic pathway and the uroporphyrinogen decarboxylase (HEME) gene in the chlorophyll biosynthetic pathway were responsible for high anthocyanin and low chlorophyll, respectively. These findings provide insights into the mechanism of purple-leaf formation in tea cultivars.

show abstract

The Quality Control of Tea by Near-Infrared Reflectance (NIR) Spectroscopy and Chemometrics

Cited by 61 publications

References 60 publications

Rapid and Nondestructive Discrimination of Geographical Origins of Longjing Tea using Hyperspectral Imaging at Two Spectral Ranges Coupled with Machine Learning Methods

Rapid and Nondestructive Discrimination of Geographical Origins of Longjing Tea using Hyperspectral Imaging at Two Spectral Ranges Coupled with Machine Learning Methods

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

Metabolic Profiling and Gene Expression Analyses of Purple-Leaf Formation in Tea Cultivars (Camellia sinensis var. sinensis and var. assamica)

Contact Info

Product

Resources

About