Abstract:The overall results demonstrated that NIR spectroscopy with pattern recognition could be successfully applied to discriminate white tea and albino tea quickly and non-destructively without the need for various analytical determinations.
“…In contrast, the remaining region, from 4000 to 7500 cm −1 , seems to be more informative, and the importance of this region will be evaluated below. It is noted that both regions were also observed in others studies performed on tea and bamboo leaves 13,32 .Figure 1NIR mean raw spectra of fresh (green dashed line) and dried-powdered (blue solid line) leaves.…”
Section: Resultssupporting
confidence: 77%
“…In the last years, NIRS has gained importance in the agriculture sector as an interesting tool for monitoring and quality assessment of agricultural products 6–8 . Various studies have been published concerning the geographic origin 9–11 and species discrimination in grapevine 12 , tea 13 , tomato 14 and coffee 15 .…”
The emergence of new almond tree (Prunus dulcis) varieties with agricultural interest is forcing the nursery plant industry to establish quality systems to keep varietal purity in the production stage. The aim of this study is to assess the capability of near-infrared spectroscopy (NIRS) to classify different Prunus dulcis varieties as an alternative to more expensive methods. Fresh and dried-powdered leaves of six different varieties of almond trees of commercial interest (Avijor, Guara, Isabelona, Marta, Pentacebas and Soleta) were used. The most important variables to discriminate between these varieties were studied through of three scientifically accepted indicators (Variable importance in projection¸ selectivity ratio and vector of the regression coefficients). The results showed that the 7000 to 4000 cm−1 range contains the most useful variables, which allowed to decrease the complexity of the data set. Concerning to the classification models, a high percentage of correct classifications (90–100%) was obtained, where dried-powdered leaves showed better results than fresh leaves. However, the classification rate of both kinds of leaves evidences the capacity of the near-infrared spectroscopy to discriminate Prunus dulcis varieties. We demonstrate with these results the capability of the NIRS technology as a quality control tool in nursery plant industry.
“…In contrast, the remaining region, from 4000 to 7500 cm −1 , seems to be more informative, and the importance of this region will be evaluated below. It is noted that both regions were also observed in others studies performed on tea and bamboo leaves 13,32 .Figure 1NIR mean raw spectra of fresh (green dashed line) and dried-powdered (blue solid line) leaves.…”
Section: Resultssupporting
confidence: 77%
“…In the last years, NIRS has gained importance in the agriculture sector as an interesting tool for monitoring and quality assessment of agricultural products 6–8 . Various studies have been published concerning the geographic origin 9–11 and species discrimination in grapevine 12 , tea 13 , tomato 14 and coffee 15 .…”
The emergence of new almond tree (Prunus dulcis) varieties with agricultural interest is forcing the nursery plant industry to establish quality systems to keep varietal purity in the production stage. The aim of this study is to assess the capability of near-infrared spectroscopy (NIRS) to classify different Prunus dulcis varieties as an alternative to more expensive methods. Fresh and dried-powdered leaves of six different varieties of almond trees of commercial interest (Avijor, Guara, Isabelona, Marta, Pentacebas and Soleta) were used. The most important variables to discriminate between these varieties were studied through of three scientifically accepted indicators (Variable importance in projection¸ selectivity ratio and vector of the regression coefficients). The results showed that the 7000 to 4000 cm−1 range contains the most useful variables, which allowed to decrease the complexity of the data set. Concerning to the classification models, a high percentage of correct classifications (90–100%) was obtained, where dried-powdered leaves showed better results than fresh leaves. However, the classification rate of both kinds of leaves evidences the capacity of the near-infrared spectroscopy to discriminate Prunus dulcis varieties. We demonstrate with these results the capability of the NIRS technology as a quality control tool in nursery plant industry.
“…Furthermore, it can extract relevant information and discover patterns in large series of data [16]. The most frequently used chemometric methods, including PCA [17], PLS-DA [13], and HCA [17], have been confirmed to be efficient and rapid methods to highlight differences among tea samples [18][19][20]. PCA is based on the concept of dimensionality reduction to transform multiple indicators into a few comprehensive ones, thereby simplifying the analysis process [21][22][23].…”
An aroma-based method for distinguishing different grades of Nongxiang Tieguanyin was explored by taking special grade (K110) and 1–4 grades (K101, K102, K103, and K104) of this tea as samples. Tea samples were analyzed by gas chromatography–mass spectrometry (GC-MS) combined with chemometrics. Results showed differences in the types and relative contents of aroma components among different grades of Nongxiang Tieguanyin tea. In the principal component analysis (PCA) scoring plot, except for K102 and K103, tea samples of different grades were distributed in different regions. Components satisfying variable important for the projection (VIP) > 1 and peak areas with significant differences (p < 0.05) among different tea grades were screened. Finally, 18 differential variables were screened out from 143 volatiles. The clustering results of these variables were consistent with those of PCA. K102 and K103 were initially clustered into one group and then clustered with K101, K110, and K104 in turn. The clear PCA separation of these samples and uniform hierarchical cluster analysis (HCA) clustering results suggests that GC-MS coupled with chemometrics analysis is a valid and accurate approach for discriminating different grades of Nongxiang Tieguanyin. The screened differential variables could represent a difference in aroma quality among five grades of Nongxiang Tieguanyin tea. Clear rules between peak area and the grade were also observed in some differential variables. 1-Ethylpyrrole and unknown-32 were positively correlated with grade. 2-Methylfuran, 2-ethylfuran, 2-methylidenecyclopentan-1-ol, mesityl oxide, 2-amylfuran, and D-limonene were negatively correlated with grade. The peak areas of methyl acetate, dimethyl sulfide, 6-methylhept-5-en-2-one, and (Z)-β-ocimene initially decreased but then increased with declining grade. The toluene content was especially high in K104 but only a negligible difference was observed among other grades. This study provides a potential method for differentiating Nongxiang Tieguanyin teas of different grades based on aroma. Unknown samples could be classified by comparison of their spatial distribution with those of known standard samples in PCA or HCA, as well as the peak area differences of differential variables between unknown samples and known standard samples.
“…In recent years, vibrational spectroscopic techniques such as infrared and Raman spectroscopy have been proved to be powerful tools for food adulteration detection (Paradkar et al 2003;Zou et al 2009;Mauer et al 2009;Chen et al 2014). A Fourier transform near-infrared (FT-NIR) spectroscopy is one of the most powerful techniques with the advantages of nondestruction measurement, simplicity of sample preparation, and high efficiency.…”
Konjac glucomannan (KGM) adulterated with cheaper starch cannot easily be identified by visual inspection. This study proposed a rapid and simple method to quantitatively analyze corn starch adulteration in KGM by a Fourier transform near-infrared (FT-NIR) coupled with chemometrics. The partial least-squares (PLS) regression calibration models based on the FT-NIR were designed using 90 samples. Coefficient of determination (R c 2 ) and root-mean-square error of PLS regression models in calibration set were found to be 0.982-0.990 and 3.596-2.693 % depending on the pretreatments of spectral data, respectively. The other 45 samples were used in the validation (30 samples) and external validation (15 samples) sets. Model 3 (using first derivative with 7 smoothing points) in the validation set yielded satisfactory performance with an R v 2 value and root-mean-square error of prediction of 0.989 and 4.890 %, respectively. The overall results indicate that FT-NIR spectroscopy could be a simple and efficient tool for the detection and quantification of the KGM adulterated with corn starch.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
