•Pocket-sized NIRS was used to evaluate tea quality qualitatively and quantitatively.•Accurate tea type discrimination and component prediction models were obtained.•Novel IVSO can improve the ...accuracies of catechin, caffeine, and theanine models.•It provided a green and low-cost method for the in-situ assessment of tea quality.
Rapid and low-cost testing tools provide new methods for the evaluation of tea quality. In this study, a micro near-infrared (NIR) spectrometer was used for the qualitative and quantitative evaluation of tea. A total of 360 tea samples consisting of black, green, yellow, and oolong tea were collected from different countries. Chemometrics including linear partial least squares (PLS) regression, PLS discriminant analysis, and nonlinear radial basis function–support vector machine (RBF–SVM) were used. The RBF–SVM model achieved optimal discriminant performance for tea types with a correct classification rate of 98.33%. Wavelength selection of iteratively variable subset optimization (IVSO) exhibited considerable advantages in improving the predictive performance of catechin, caffeine, and theanine models. The IVSO–PLS regression models achieved satisfactory results for catechins and caffeine prediction, with Rp over 0.9, and RPD over 2.5. Thus, the study provided a portable and low-cost method for in-situ assessing tea quality.
Artemisia sieberi is widely distributed in the desert and semi-desert regions of Iran. We collected samples from different parts of Iran and proceeded to extract and analyse their essential oils ...using hydrodistillation and GC-MS, respectively. Among seventy-two compounds identified within the oils, the hydrocarbon and oxygenated monoterpenoids, trans-thujone (0.0–22.9%), cis-thujane (0.0–47.3%), 1, 8-cineole (0.7–37.1%), camphor (0.0–46.4%), santolinyl acetate (0–33.8%) and cis-chrysanthenyl acetate (0.0–16.4%) and the sesquiterpenoid, davanone (0.0–59.6%), were reported as the predominant components from the 17-accession. The above-mentioned GC-MS analytical results in conjunction with chemometric calculations, including Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA), suggested 6 chemical groups of A. sieberi collected from the Northern to the Southern parts of Iran. The chemical classification of EOs were based on the sum of concentration of terpenoids with distinct C-skeletons, but not individual constituents. These distinct groups include the species predominant in, I: thujane, II: davanone, III: davanone and bornane, IV: p-menthane, V: bornane and VI: irregular monoterpenoids. The trace or less distributed phytochemicals are also suggested to divide the A. sieberi into two main group of sesquiterpene and monoterpene producers.
•Seventeen essential oil samples of Artemisia sieberi from Iran have been analysed by GC-MS.•Among seventy-two identified compounds mono- and sesquiterpenoids were the main components.•Using PCA and HCA six chemotypes suggested for A. sieberi in different area of Iran.•Trace and less distributed phytochemicals were used to classify plant's essential oils.
Lead ion (Pb2+) is a main heavy metal in food that causes heavy teratogenicity and carcinogenicity. In this study, a rapid and sensitive SERS method for detecting Pb2+ in food was established by ...aptamer regulating gold nanoparticles reduction. The reduction of HAuCl4 catalyzed by H2O2 is a slow process, and graphene oxide (GO) has excellent catalytic performance for the reaction, which enabled the system to generate gold nanoparticles (AuNPs) with high Raman activity. When the aptamer was introduced into the system, its binding with GO reduced the reaction speed. Upon adding Pb2+ to the system, the aptamer preferentially combined with Pb2+ and GO was released to accelerate the AuNPs production. The concentration of the AuNPs was proportional to the intensity of the added Raman signal molecule 4-MBA and the main Raman peak of Pb2+ appeared at 1595.80 cm−1. The ability of a novel aptamer (M4-16) and traditional aptamers (T30695, TBA) for Pb2+ determination was compared, and the concentration of the aptamer, HAuCl4 and heating time were optimized to build optimal detection system. After several pretreatment of the original SERS spectroscopy, combined with the comparison of various models, the first-order derivative preprocessing combined with competitive adaptive reweighted sampling model achieved the best performance (Rc = 0.9966, Rp = 0.9972), the detection limit for Pb2+ was 0.1 μg L−1. The combination of SERS technology and chemometrics is a promising method that could be used to achieve rapid and highly sensitive detection of Pb2+ in food.
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•Lead in food was quantified using SERS regulated by novel aptamer.•Spectral pretreatments improved the performance of the PLS model.•CARS-PLS model has high accuracy and predictive power.•SERS spectroscopy is efficient technique for safety determination.
Soluble solids content (SSC) serves as a crucial metric in assessing loquat quality. To achieve fast, non-destructive, and accurate detection of SSC, the combination of near-infrared (NIR) ...spectroscopy and broad learning system (BLS) model was developed in this study. Meanwhile, the influence of six spectral preprocessing methods and six wavelength selection algorithms on SSC prediction was explored, and the performance of the BLS model was compared with the widely used partial least squares regression (PLSR) and least squares support vector machine (LS-SVM) models. The results showed that Savitzky-Golay smoothing combined with standard normal transformation (SG-SNV) offered the optimal pretreatment, and the hybrid method, namely interval variable iterative space shrinking analysis and successive projections algorithm (iVISSA-SPA), emerged as the most effective wavelength selection method. The BLS model outperformed both PLSR and LS-SVM models. Specifically, the BLS model based on the iVISSA-SPA method achieved the optimal SSC prediction results, with RP2 = 0.8646, RMSEP = 0.5104, and RPD = 2.7481. Therefore, NIR spectroscopy coupled with the BLS model and hybrid wavelength selection strategy could rapidly, non-destructively and accurately detect the SSC of loquat, providing a viable alternative for SSC prediction in fruit.
•NIR spectroscopy was an efficient tool for detecting SSC in loquat.•iVISSA-SPA was a powerful combination for selecting the key wavelengths.•BLS model performed better than PLSR and LS-SVM models.•iVISSA-SPA-BLS model provided the best prediction results.
Shaking is a critical process in the formation of oolong tea quality, although the metabolites and their changes in this sensitive process have not yet been determined. In this study, untargeted ...analysis based on ultrahigh-performance liquid chromatography/quadrupole–Orbitrap mass spectrometry was conducted to comprehensively profile metabolite changes in different cultivars. Theanine glucoside was identified for the first time in oolong tea. Hierarchical cluster analysis indicated that shaking caused major changes in metabolite levels in oolong tea. Seventy-one, 83 and 73 potential features showed significant differences between pre- and post-shaking samples for var. sinensiscv. “Zimudan”, “Shuixian”, and “Huangmeigui,” respectively. Chemometrics analysis of the three cultivars led to the identification of 18 shared metabolites, including epigallocatechin gallate, phenylalanine, tryptophan, proline, and hydroxy-jasmonic acid, as potential markers. This study identified the metabolites that allow monitoring of tea quality formation during both processing and preservation, and it provides a novel strategy for data reduction in studies to discover key metabolites.
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•Shaking led to major changes in the metabolite profile of oolong tea.•Theanine glucoside was identified for the first time in oolong tea.•For the discovery of tea markers, metabolite profiling of three cultivars was performed.•Eighteen shared metabolites were identified as potential markers of oolong tea.•Dynamic changes and the correlation of potential markers were examined.
Cinnamon (Cinnamomum sp.) is a commonly used spice that is at risk of being counterfeited due to its intricate production process, which can compromise its integrity. This study aimed to develop a ...rapid detection method using Near Infrared (NIR) and Mid Infrared (MIR) spectroscopy, along with chemometrics, to identify fraud in genuine cinnamon powder. Various techniques, including exploratory, non-targeted, and targeted methods, were used to detect the presence of adulterants such as coffee husks and corn meal in cinnamon, with concentrations ranging from 10% to 50%. The exploratory and non-targeted models successfully identified the potential occurrence of fraud as a result of the addition of coffee husks and corn meal. The models' effectiveness was tested on commercial cinnamon samples, exposing a high proportion of potentially fraudulent samples. Adulteration percentages exceeded 60% for the presence of coffee husks and 50% for corn meal. The results show that NIR and MIR spectroscopy, when used with chemometrics, are effective for monitoring the quality of cinnamon powder.
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•NIR and MIR spectroscopy were effective in detecting fraud in cinnamon.•Exploratory models made it possible to identify fraud in cinnamon powder samples.•The PLS models successfully predicted the presence of adulterants in unknown samples.
The increased interest in whole-grain products, along non unified European regulations on the composition of wholemeal bread could lead to its misleading labelling. Therefore, to ensure the safety of ...consumers, both health-wise and from possible fraud, a novel hyperspectral imaging-based authentication method is being proposed, given that no previous study has combined imaging techniques with authentication of wholemeal flour content. Quantification based on pixel counting by classification (QPC) utilizes multivariate analysis methods such as PLS-DA and SVM to classify pixels within a bread sample (as Wholemeal and White flour), based on their visible-near-infrared spectra, to later estimate its proportion of wholemeal flour. This is in accordance with the heterogeneous nature of bread samples, in which individual pixels belonging to both wholemeal and white flour can be accounted for, which was proved by implementing unsupervised training techniques such as hierarchical cluster analysis (HCA). Results show that the quantification model was able to successfully predict wholemeal flour content with a maximum deviation of 8 g wholemeal flour/100 g flour from the estimated value.
•Sustainable approach for quality control of wholemeal bread.•A novel quantification methodology based on pixel counting was introduced.•Verification of bread samples based on wholemeal flour content.•Hyperspectral imaging together to chemometrics for bread quality control was employed.
First, the time-domain spectral signals, frequency-domain spectral signals, and absorption coefficient spectral signals were collected, and the whole spectra were preprocessed by applying multiple ...preprocessing methods. Then, a special algorithm of uninformative variable elimination (UVE) is used to find the effective variables and remove the useless variables from the preprocessed spectrum. Finally, the PLS-DA, LS-SVM and BPNN analysis models for wheat producing areas discrimination were built based on such characteristic information, with their accuracy analyzed.
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•Terahertz spectroscopy combined with chemometrics methods was used to detect the geographical origin of wheat sample, the comprehensive discrimination accuracy of wheat from different origins by the SNV-LS-SVM model reached 96.76%.•Uninformative variables elimination (UVE) was used to select the frequency variables of terahertz spectrum, which reduces the amount of spectral data.•Analysis and compare the results of the detection models of the terahertz spectral data after processing.
Wheat from different producing areas has different flavor and properties, and thus the identification of producing area of wheat is significant to assure the quality of wheat. The traditional method of producing area of wheat determination is time-consuming, complex and needs a lot of pretreatment. The purpose of this research is to develop a new method for the determination of wheat producing areas by terahertz time domain spectroscopy in combination with chemometrics. Firstly, a total of 240 wheat samples from Shandong Province, Shaanxi Province, Henan Province, Hebei Province and Anhui Province of China were collected to analyze and obtain the time-domain spectral signals, frequency-domain spectral signals, and absorption coefficient spectral signals of the samples were obtained. Then, four different preprocessing methods of Savitzky-Golay (S-G), multiplicative scatter correction (MSC), mean centering, and standard normal variate (SNV) were applied to preprocess the absorption coefficient spectral signals, and the uninformative variable elimination (UVE) was used for variable selection of THz spectra data, for developing an effective prediction model. Finally, chemometrics methods, including the partial least squares discriminant analysis (PLS-DA), back propagation neural network (BPNN) and least squares support vector machines (LS-SVM) qualitative models were used for model building and discrimination results obtained through such models were compared. According to the test results, the comprehensive discrimination accuracy of wheat from different origins by the SNV-LS-SVM model reached 96.76%, Furthermore, these results demonstrated that an accurate qualitative analysis of producing area of wheat samples could be achieved by terahertz time-domain spectroscopy combined with chemometrics, which can provide a fast and accurate solution for grain security detection and origin tracing.
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