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Hangbaiju is highly appreciated flower tea for its health benefits, and its quality and price are affected by geographical origin. Fast and accurate identification of the geographical origin of Hangbaiju is very significant for producers, consumers and market regulators. In this work, hyperspectral imaging combined with chemometrics, was used, for the first time, to explore and implement the geographical origin classification of Hangbaiju. The hyperspectral images in the spectral range of 410-2500 nm for 75 samples of five different origins were collected. As a versatile chemometrics tool, bagging classification tree-radial basis function (BAGCT-RBFN), compared with classification tree (CT), radial basis function network (RBFN), was applied to discriminate Hangbaiju samples from different origins. The results showed that BAGCT-RBFN based on optimal wavelengths yielded superior classification performances to CT and RBFN with full wavelengths. The recognition rates (RR) of the training and prediction sets by BAGCT-RBFN were 96.0 % and 92.0 %, respectively. Hyperspectral imaging combined with chemometric can be considered as a powerful, feasible and convenient tool for the classification of Hangbaiju samples from different origins. It promises to be a potential way for origin discriminant analysis and quality monitor in food fields., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Carbaryl is a typical carbamate pesticide that plays an essential role in agricultural production, but its residues cause serious harm to the environment and human health. Here, we developed a polychromatic colorimetric sensor based on ZnTPyP-DTAB peroxidase activity and gold nano-bipyramids (Au NBPs) etching to detect carbaryl. ZnTPyP-DTAB catalyzes the decomposition of H 2 O 2 to hydroxyl radicals, and Au NBPs are etched. The coordination of zinc and nitrogen in nanometer porphyrins was affected by the steric effects of carbaryl, which resulted in decreased activity of ZnTPyP-DTAB peroxidase. The detection limit of carbaryl was 0.26 mg/kg. The recoveries of carbaryl in reaal sample ranged from 91 % to 107% (RSD ≤ 0.7%). The sensor platform displayed a series of high-resolution multicolor variations of rainbow colors within the above concentration range. The rich color variation facilitates the acquisition of digital images. RGB value transformation combined with partial least squares regression model can accurately and quantitatively detect carbaryl in vegetables, fruits and Chinese medicinal materials., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Atractylodes macrocephala Koidz (AMK) is an expensive edible Chinese herb with medicinal properties. Its economic value and medicinal properties are closely related to its geographical origin. In this study, a method based on stable isotopes and multiple elements combined with chemometrics was developed to identify the geographical origin of AMK. Five stable isotope ratios, including δ2H, δ18O, δ13C, δ15N, and δ34S, and 41 elements in 281 AMK samples from 10 regions were analyzed. An analysis of variance of stable isotope ratios and elements revealed that the δ2H, δ18O, Mg, Ca, and rare‐earth element concentrations in AMK from different geographical regions were significantly different. Orthogonal partial least squares discriminant analysis proved that Ca, K, Mg, and Na can be used for classifying (variable importance >1) and accurately identifying AMK from Panan, Xianfeng, and other areas with 100% discrimination accuracy. In addition, we achieved a good identification of protected geographic indication products of similar quality. This method realized the geographical discrimination of AMK from different producing areas and could potentially control the fair trade of AMK. Practical Application: The quality of AMK is highly dependent on its geographical origin. Confusion over the origin of AMK impacts consumer rights. This study developed an accurate and effective classification method based on stable isotopes and multiple elements to ascertain the geographical origin of AMK, thereby providing an effective method for determining its quality. [ABSTRACT FROM AUTHOR]

As one of the foods commonly eaten all over the world, eggs have attracted more and more attention for their quality and price. A method based on elemental profiles and chemometrics to discriminate between free-range and caged eggs was established. Free-range (n1 = 127) and caged (n2 = 122) eggs were collected from different producing areas in China. The content of 16 elements (Zn, Pb, Cd, Co, Ni, Fe, Mn, Cr, Mg, Cu, Se, Ca, Al, Sr, Na, and K) in the eggshell was determined using a inductively coupled plasma atomic emission spectrometer (ICP-AES). Outlier diagnosis is performed by robust Stahel–Donoho estimation (SDE) and the Kennard and Stone (K-S) algorithm for training and test set partitioning. Partial least squares discriminant analysis (PLS-DA) and least squares support vector machine (LS-SVM) were used for classification of the two types of eggs. As a result, Cd, Mn, Mg, Se, and K make an important contribution to the classification of free-range and caged eggs. By combining column-wise and row-wise rescaling of the elemental data, the sensitivity, specificity, and accuracy were 91.9%, 91.1%, and 92.7% for PLS-DA, while the results of LS-SVM were 95.3%, 95.6%, and 95.1%, respectively. The result indicates that chemometrics analysis of the elemental profiles of eggshells could provide a useful and effective method to discriminate between free-range and caged eggs. [ABSTRACT FROM AUTHOR]

Chrysanthemi Flos 'Hangbaiju' (HBJ) is a common Chinese medicinal material with the same origin as the medicinal and edible cognate plant in China, whose quality is seriously affected by the place of origin. In this study, four stable isotope ratios (δ15N, δ2H, δ13C, and δ18O) and 44 elements were detected and analyzed in 191 HBJ flower samples from six locations in China to trace the origin of HBJ. An ANOVA analysis of δ15N, δ2H, δ13C, and δ18O values, as well as milti-elements, showed that there were significant differences among the six places of origin. Partial least squares discriminant analysis (PLSDA) and one-class partial least squares discriminant analysis (OPLS-DA) models were established to trace the origin of HBJ from these six locations. The results showed that the classification effect of the PLSDA model is poor; however, the established OPLS-DA model can distinguish between products of national geographic origin (Tongxiang City, Zhejiang Province, China) and samples from other origins, among which Ni, Mo, δ13C, Cu, and Ce elements (VIP > 1) contribute the most to this classification. Therefore, this study provides a new method for tracing the origins of HBJ, which is of great significance for the protection of origin labeling of products. [ABSTRACT FROM AUTHOR]

A method based on elemental fingerprint, stable isotopic analysis and combined with chemometrics was proposed to trace the geographical origins of Licorice (Glycyrrhiza uralensis Fisch) from 37 producing areas. For elemental fingerprint, the levels of 15 elements, including Ca, Cu, Mg, Pb, Zn, Sr, Mn, Se, Cd, Fe, Na, Al, Cr, Co, and K, were analyzed by inductively coupled plasma atomic emission spectrometry (ICP-AES). Three stable isotopes, including δ13C, δ15N, and δ18O, were measured using an isotope-ratio mass spectrometer (IRMS). For fine classification, three multiclass strategies, including the traditional one-versus-rest (OVR) and one-versus-one (OVO) strategies and a new ensemble strategy (ES), were combined with two binary classifiers, partial least squares discriminant analysis (PLSDA) and least squares support vector machines (LS-SVM). As a result, ES-PLSDA and ES-LS-SVM achieved 0.929 and 0.921 classification accuracy of GUF samples from the 37 origins. The results show that element fingerprint and stable isotope combined with chemometrics is an effective method for GUF traceability and provides a new idea for the geographical traceability of Chinese herbal medicine. [ABSTRACT FROM AUTHOR]

BACKGROUND: Sesame oil has an excellent flavor and is widely appreciated. It has a higher price than other vegetable oils because of the high price of its raw materials, and different processing techniques also result in products of different quality levels, which can command different prices. In the market, there is a persistent problem of adulteration of sesame oil, driven by economic interests. The screening of volatile markers used to distinguish the authenticity of sesame oil raw materials and production processes is therefore very important. RESULTS: In this work, six markers related to the production processes and raw materials of sesame oil were screened by gas chromatography–tandem mass spectrometry (GC‐MS/MS) combined with chemometric analysis. They were 3‐methyl‐2‐butanone, 2‐ethyl‐5‐methyl‐pyrazine, guaiacol, 2,6‐dimethyl‐pyrazine, 5‐methyl furfural, and ethyl‐pyrazine. The concentration of these markers in sesame oil is between 10 and1000 times that found in other vegetable oils. However, only 3‐methyl‐2‐butanone and 2‐ethyl‐5‐methyl‐pyrazine differed significantly as the result of the use of different production processes. Except for guaiacol, which was mainly derived from raw materials, the other five compounds mentioned above all result from the Maillard reaction during thermal processing. The six compounds mentioned above are sufficient to distinguish fraud involving sesame oil raw materials and production processes, and can identify accurately adulteration levels of 30% concentration. CONCLUSION: In this study, the classification markers can identify the adulteration of sesame oil accurately. These six compounds are therefore important for the authenticity of sesame oil and provide a theoretical basis for the rapid and accurate identification of the authenticity of sesame oil. © 2021 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Elemental fingerprint, stable isotopic analysis, and chemometrics were combined to identify organic kiwifruit from ordinarily cultivated kiwifruit. Samples of organic (n1 = 78) and ordinary kiwifruit (n2 = 85) were collected from neighboring areas. For elemental fingerprint, the contents of 15 elements in fresh fruits, including Al, Cr, Mg, Pb, Zn, Ca, Cu, Mn, Se, Cd, Fe, Na, Sr, Co, and K, were determined by inductively coupled plasma optical emission spectrometry (ICP‐OES). Three stable isotopes, including δ13C, δ15N, and δ18O, were analyzed using an isotope‐ratio mass spectrometer (IRMS). Different classification methods including soft independent modeling of class analogy (SIMCA), partial least squares discriminant analysis (PLSDA), and least squares support vector machines (LS‐SVM), were used to discriminate the organic and ordinary kiwifruits by fusion of elemental and stable isotopic. As a result, the sensitivity, specificity, and overall accuracy of SIMCA model were 0.885, 0.857, and 0.864, respectively. PLSDA and LS‐SVM obtained 0.950 and 0.983 classification accuracy of organic and ordinary kiwifruits, respectively. It was demonstrated that elemental fingerprint and stable isotopic analysis would provide useful chemical information for the identification of organic fruits, and the capacity of these methods could be enhanced by chemometrics. Practical Application: The classification of kiwifruit usually relies on the label assigned by the merchant, which is prone to deceive consumers. This research has developed an accurate and effective classification method based on stable isotopes and mineral elements for the identification of ordinary kiwifruit and organic kiwifruit, providing a tool for the quality monitoring of organic food. [ABSTRACT FROM AUTHOR]

BACKGROUND: The quality of tea is influenced by numerous factors, especially l‐theanine, which is one of the important markers used to evaluate the sweetness and freshness of tea. Sensitive, rapid, and accurate detection of l‐theanine is therefore useful to identify the grade and quality of tea. RESULTS: A high‐sensitivity, paper‐based fluorescent sensor combined with chemometrics was established to detect l‐theanine in tea water based on CdTe quantum dots / corn carbon dots and nano tetra pyridel‐porphine zinc (ZnTPyP). To verify the reliability of this method, fluorescence spectra and fluorescence‐visualized paper‐based sensors were compared. The fluorescence spectrum method demonstrated a linear range of 1 to 10 000 nmol L−1 and a limit of detection (LOD) of 0.19 nmol L−1. In the fluorescence‐visualized paper‐based sensors there was a linear range of 10–1000 nmol L−1, and the LOD was 10 nmol L−1. Partial least squares discriminant analysis (PLSDA) and partial least squares regression analysis (PLSR) were used successfully to determine l‐theanine accurately in tea water with this approach. The accuracy of the PLSDA model was 100% both in the training set and the predicting set, and the correlation coefficient between the actual concentration and the predicted concentration was greater than 0.9997 in the PLSR model. CONCLUSION: This fluorescence‐visualized paper‐based sensor, combined with chemometrics, could be applied efficiently to the practical analysis of tea water samples, which provides a new idea to ensure the flavor and quality of tea. © 2020 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

BACKGROUD Pesticides are widely used to control insect infestation and weeds in agriculture. However, concerns about the pesticide residues in agricultural products have been raised in recent years because of public interest in health and food quality and safety. Thus, rapid, convenient, and accurate analytical methods for the detection and quantification of pesticides are urgently required. RESULTS: A nanohybrid system composed of gold nanoparticles (AuNPs) and tetrakis(N‐methyl‐4‐pyridiniumyl) porphyrin (TMPyP) was used as an optical probe for the detection and quantification of five pesticides (Paraquat, Dipterex, Dursban, methyl thiophanate and Cartap). The method is based on the aggregation effect of pesticides on the carboxyl group modified by AuNPs. Subsequently, with the help of particle swarm optimization‐optimized sample weighted least squares‐support vector machine (PSO‐OSWLS‐SVM), all the pesticides could be successfully quantified. In addition, partial least squares discriminant analysis (PLS‐DA) was applied and the five pesticides were satisfactorily recognized based on data array obtained from the ultraviolet visible (UV‐visible) spectra of AuNP–TMPyP complex. Furthermore, the quantitative and qualitative analysis of the five pesticides could be also achieved in the complex real samples, in which all the relative standard deviations (RSDs) were less than 0.3‰ and all the linear absolute correlation coefficients were more than 0.9990. Furthermore, recognition rate of the training set and the prediction set based on multiplicative scatter correction (MSC), or second‐order derivative (2nd derivative) UV‐visible spectra in PLS‐DA model could reach 100%. CONCLUSION: This method was successfully applied for the rapid and accurate determination of multicomponent pesticide residues in real food samples. © 2020 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

BACKGROUND The fluorescence sensing method has been increasingly applied in food quality control because it is fast and sensitive. However, its application in quality evaluation is challenging. Using Citri Reticulatae Pericarpium (CRP; dried mandarin orange peel) as an example, we developed a simple and low‐cost fluorescence sensing strategy based on nanoparticles combined with spectral splicing and chemometrics for quality evaluation. This method can recognize Citrus reticulata 'Chachi' (CRC) from other CRP cultivars and further identify the storage year. RESULTS: Nanogold particles and cadmium telluride quantum dots were selected as nanosensors and mixed with aqueous extracts of CRP separately to produce fluorescence quenching spectra. Then, a simple spectral splicing procedure was applied to obtain spliced spectra comprising different combinations of the self‐fluorescence and fluorescence quenching spectra of CRP samples. With the aid of partial least‐squares discriminant analysis, the new strategy achieved recognition rates of 100% in distinguishing CRC samples from other CRP samples, as well as recognition rates of 100% for the training set and 98.04% for the prediction set in the discrimination of the storage year of CRC. The recognition mechanism is dominated by interactions between the nanoparticles and the fluorescent components in the CRP samples, but other components also have concurrent effects. CONCLUSIONS: This novel fluorescence sensing strategy not only provides a new tool for the quality evaluation of CRC but also has good prospects for the authentication and traceability of other foods and herbs. Crucially, the developed method is convenient, simple and effective. © 2020 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Fourier transform near-infrared (NIR) spectroscopy and mid-infrared (MIR) spectroscopy play important roles in all fingerprint techniques because of their unique characteristics such as reliability, versatility, precision, and ease of measurement. In this paper, a supervised pattern recognition method based on the PLSDA algorithm by NIR and the NIR-MIR fusion spectra has been established to identify geoherbalism of Angelica dahurica from different regions and authenticity of Corydalis yanhusuo W. T. Wang. Comparing principle component analysis (PCA) cannot successfully identify geographical origins of Angelica dahurica. Linear discriminant analysis (LDA) also hardly distinguishes those origins. Furthermore, the PLSDA model based on the data fusion of NIR and IR was more accurate and efficient. But, the identification of authenticity of Corydalis yanhusuo W. T. Wang was still inaccurate in the PLSDA model. Consequently, data fusion of NIR-MIR original spectra combined with moving window partial least-squares discriminant analysis was firstly used and showed perfect properties on authenticity and adulteration discrimination of Corydalis yanhusuo W. T. Wang. It indicated that data fusion of NIR-MIR spectra combined with MWPLSDA could be considered as the promising tool for rapid discrimination of the geoherbalism and authenticity of more Chinese herbs in the future. [ABSTRACT FROM AUTHOR]

In this paper, mid- and near-infrared spectroscopy fingerprints were combined to simultaneously discriminate 12 famous green teas and quantitatively characterize their antioxidant activities using chemometrics. A supervised pattern recognition method based on partial least square discriminant analysis (PLSDA) was adopted to classify the 12 famous green teas with different species and quality grades, and then optimized sample-weighted least-squares support vector machine (OSWLS-SVM) based on particle swarm optimization was employed to investigate the quantitative relationship between their antioxidant activities and the spectral fingerprints. As a result, 12 famous green teas can be discriminated with a recognition rate of 100% by MIR or NIR data. However, compared with individual instrumental data, data fusion was more adequate for modeling the antioxidant activities of samples with RMSEP of 0.0065. Finally, the performance of the proposed method was evaluated and validated by some statistical parameters and the elliptical joint confidence region (EJCR) test. The results indicate that fusion of mid- and near-infrared spectroscopy suggests a new avenue to discriminate the species and grades of green teas. Moreover, the proposed method also implies other promising applications with more accurate multivariate calibration of antioxidant activities. [ABSTRACT FROM AUTHOR]

The origins and authenticity against frauds are two essential aspects of food quality. In this work, a comprehensive quality evaluation method by FT-NIR spectroscopy and chemometrics were suggested to address the geographical origins and authentication of Chinese Ganoderma lucidum (GL). Classification for 25 groups of GL samples (7 common species from 15 producing areas) was performed using near-infrared spectroscopy and interval-combination One-Versus-One least squares support vector machine (IC-OVO-LS-SVM). Untargeted analysis of 4 adulterants of cheaper mushrooms was performed by one-class partial least squares (OCPLS) modeling for each of the 7 GL species. After outlier diagnosis and comparing the influences of different preprocessing methods and spectral intervals on classification, IC-OVO-LS-SVM with standard normal variate (SNV) spectra obtained a total classification accuracy of 0.9317, an average sensitivity and specificity of 0.9306 and 0.9971, respectively. With SNV or second-order derivative (D2) spectra, OCPLS could detect at least 2% or more doping levels of adulterants for 5 of the 7 GL species and 5% or more doping levels for the other 2 GL species. This study demonstrates the feasibility of using new chemometrics and NIR spectroscopy for fine classification of GL geographical origins and species as well as for untargeted analysis of multiple adulterants. [ABSTRACT FROM AUTHOR]

[Display omitted] • Stable isotope and multi-element compositions of Huangjing was used for its origin traceability. • OPLS-DA significantly enhanced the traceability capability for Qingyang Huangjing products. • The discrimination accuracies of Qingyang (PGI products) were 100 % compared with other products. • Regional and farming patterns the correlations with eco-environment were discussed. • The strategy can be applied to combat mislabeling of PGI Huangjing products and ensure food safety. Huangjing (Polygonati Rhizoma) is a high-value homology of medicine and food in China, for which the geographical origins (i.e., geoherbalism) and farming patterns (natural or farmed) are decisive in its functions and selling price. In this study, five stable isotope ratios (δ 13C, δ 15N, δ 18O, δ 2H, and 87Sr/86Sr) and 48 multi-element signatures of 238 Huangjing samples collected from seven main production areas in China were analyzed together for origin traceability and farming authentication purposes. An analysis of variance (ANOVA) for each variable revealed that δ 18O, δ 2H, 87Sr/86Sr, Fe, and rare earth elements were significantly different among origins. The orthogonal partial least squares discriminant analysis (OPLS-DA) modeling of all variables indicated that K, Ca, Mg, Fe, and Mn were the most important variables (VIP > 1) for identifying Qingyang Huangjing protected geographical indication (PGI) from those of other origins, and discriminant accuracy reached 100 %. Moreover, ANOVA and OPLS-DA demonstrated that δ 15N, K, Ca, and Mg were the elements that effectively distinguished natural and farmed Huangjing. Therefore, the strategy may be an important alternative tool for authenticity management, which is significant for combating origin mislabeling and the fraudulent conducts of Huangjing products. [ABSTRACT FROM AUTHOR]

Highlights • Untargeted metabolic analysis of Tieguanyin tea was developed. • Metabolites of teas with different seasons/years were analyzed by UPLC–Q-TOF/MS. • The differences of chemical components were revealed by heat map analysis. Abstract The taste and aroma quality of Tieguanyin tea fluctuate seasonally and yearly. However, the compounds responsible for the seasonal and year variations of metabolic pattern and its sensory quality are far from clear. 60 Tieguanyin tea samples harvested in different years and seasons were analyzed by ultraperformance liquid chromatography–quadrupole time-of-flight mass spectrometry (UPLC–Q-TOF/MS) and chemometrics. Principal component analysis (PCA) explained 33.2% of the total variance, while orthogonal projection to latent structures discriminate analysis (OPLS-DA) can obtain potential metabolites with better discrimination, and with R2X and Q2 of cross-validation as 0.974 and 0.937, respectively. Subsequently, heat map analysis (HCA) visualized relationships between Tieguanyin teas with these significantly different potential metabolites by Mann-Whitney U test (p < 0.05). Furthermore, the best discriminate metabolites contributing to different sensory qualities were revealed by stepwise liner discrimination analysis (SLDA) with 100% accuracy rate. The present strategy also exhibited great potential for untargeted metabolomics of other foods. [ABSTRACT FROM AUTHOR]

Fluorescent “turn-off” sensors based on double quantum dots (QDs) has attracted increasing attention in the detection of many materials due to their properties such as more useful information, higher fluorescence efficiency and stability compared with the fluorescent “turn-off” sensors based on single QDs. In this work, highly sensitive and specific method for recognition of 53 different famous green teas was developed based on the fluorescent “turn-off” model with water-soluble ZnCdSe-CdTe double QDs. The fluorescence of the two QDs can be quenched by different teas with varying degrees, which results in the differences in positions and intensities of two peaks. By the combination of classic partial least square discriminant analysis (PLSDA), all the green teas can be discriminated with high sensitivity, specificity and a satisfactory recognition rate of 100% for training set and 100% for prediction set, respectively. The fluorescent “turn-off” sensors based on the single QDs (either ZnCdSe QDs or CdTe QDs) coupled with PLSDA were also employed to recognize the 53 famous green teas with unsatisfactory results. Therefore, the fluorescent “turn-off” sensors based on the double QDs is more appropriate for the large-class-number classification (LCNC) of green teas. Herein, we have demonstrated, for the first time, that so many kinds of famous green teas can be discriminated by the “turn-off” model of double QDs combined with chemometrics, which has largely extended the capability of traditional fluorescence and chemometrics, as well as exhibits great potential to perform LCNC in other practical applications. [ABSTRACT FROM AUTHOR]

Fluorescent “turn-off” sensors based on water-soluble quantum dots (QDs) have drawn increasing attention owing to their unique properties such as high fluorescence quantum yields, chemical stability and low toxicity. In this work, a novel method based on the fluorescence “turn-off” model with water-soluble CdTe QDs as the fluorescent probes for differentiation of 29 different famous green teas is established. The fluorescence of the QDs can be quenched in different degrees in light of positions and intensities of the fluorescent peaks for the green teas. Subsequently, with aid of classic partial least square discriminant analysis (PLSDA), all the green teas can be discriminated with high sensitivity, specificity and a satisfactory recognition rate of 100% for training set and 98.3% for prediction set, respectively. Especially, the “turn-off” fluorescence PLSDA model based on second-order derivatives (2nd der) with reduced least complexity (LVs = 3) was the most effective one for modeling. Most importantly, we further demonstrated the established “turn-off” fluorescent sensor mode has several significant advantages and appealing properties over the conventional fluorescent method for large-class-number classification (LCNC) of green teas. This work is, to the best of our knowledge, the first report on the rapid and effective identification of so many kinds of famous green teas based on the “turn-off” model of QDs combined with chemometrics, which also implies other potential applications on complex LCNC classification system with weak fluorescence or even without fluorescence to achieve higher detective response and specificity. [ABSTRACT FROM AUTHOR]

[Display omitted] • A method for predicting species, geographical origin and main compounds of Gancao. • The method was based on 7 components resolved from three-dimensional fluorescence. • The fluorescence resolved and prediction algorithm were ATLD and random forest. • The accuracy of this geographical origin discriminant model can reach 94.4%. • The accuracy of main compounds prediction model of Gancao could reach 95.6%. Glycyrrhizae Radix et Rhizoma (Gancao) is a functional food whose quality varies significantly between distinct geographical sources owing to the influence of genetics and the geographical environment. This study employed three-dimensional fluorescence coupled with alternating trilinear decomposition (ATLD) and random forest (RF) algorithms to rapidly predict Gancao species, geographical origins, and primary constituents. Seven fluorescent components were resolved from the three-dimensional fluorescence of the ATLD for subsequent analysis. Results indicated that the RF model distinguished Gancao from various species and origins better than other algorithms, achieving an accuracy of 94.4 % and 88.9 %, respectively. Furthermore, the RF regressor algorithm was used to predict the concentrations of liquiritin and glycyrrhizic acid in Gancao, with 96.4 % and 95.6 % prediction accuracies compared to HPLC, respectively. This approach offers a novel means of objectively evaluating the origin of food and holds substantial promise for food quality assessment. [ABSTRACT FROM AUTHOR]

[Display omitted] • A visual sensor was developed to identify the origins and growth years of LB. • Catechins in LB combined with Au-AgNCs to produce AIE effect. • DD-SIMCA and PLSDA models achieved 100% accuracy of the origins and growth years. • This strategy has great potentials for identifying origins of LB and other foods. In this study, we developed a cost-effective fluorescence visual sensor strategy based on gold and silver nanocluster (Au-AgNCs) for the rapid identification of the origins and growth years of Lilium bulbs (LB). Au-AgNCs combined with catechins in LB produce aggregation-induced emission (AIE). The catechin content in LB of different origins and growth years varied, resulting in different fluorescence color responses of the sensor system. Furthermore, the RGB values of the fluorescent color were extracted, and the discriminant effect of visual visualisation was verified using the data-driven soft independent modelling of class analogy (DD-SIMCA) and partial least squares discriminant analysis (PLSDA) models. The results showed that the accuracy of DD-SIMCA for identifying LB origins and PLSDA for growth year identification was 100%. These results indicated that the established strategy could accurately identify the quality of LB, which has great potential for application in the rapid and visual identification of other foods. [ABSTRACT FROM AUTHOR]

The price and quality of goji berries (GB) are closely linked to their origins. Therefore, identifying the geographical origins of GB produced in specific origin is important. In this work, a fluorescent sensor based on a glutathione modified quantum dots and nitrogen-doped carbon quantum dots complexes (CdTe-QDs @ N-CDs) with an internal filtration effect (IFE) was constructed to visual identify the origin of GB. The phenolic acid compounds in GB could quench the fluorescence of QDs and CDs through the photoinduced electron transfer (PET) and the aggregation-induced quenching (ACQ), respectively. Due to the different contents of phenolic acid in GB from different origins, the fluorescent sensor based on CdTe-QDs @ N-CDs complexes could produce distinct and different fluorescent colors, from orange to blue under the dual reaction. Finally, partial least squares discriminant analysis (PLS-DA) model was applied to establish classification models for identifying the geographical origin of GB based on the fluorescent colors. The results showed that the accuracy of GB visual recognition could be increased from 68.9% to 93.3%. This proven that the proposed strategy has excellent potentials for identification of the geographical origins of GB. [Display omitted] • A novel visual fluorescent sensing method was constructed for identified GB origins. • CdTe-QDs@N-CDs enables visual identification of GB origin through PET & ACQ effects. • The accuracy of GB origin identification based on RGB data was 93.3%. [ABSTRACT FROM AUTHOR]

Food safety is an important public health issue, and deep learning (DL) algorithms can provide powerful tools and methods for food safety and authenticity detection. Compared with chemometric algorithms and traditional machine learning algorithms, the performances of DL algorithms are improved in many aspects. By learning and analyzing a large amount of data, DL models can improve the efficiency and accuracy of food safety and authenticity detection, helping to ensure the public health and safety. This paper reviews some commonly used chemometric algorithms, traditional machine learning algorithms, and popular DL algorithms. Among them, special attentions are paid to convolutional neural network (CNN), fully convolutional network (FCN) and generative adversarial network (GAN). Moreover, the auxiliary effect of GAN on CNN is highlighted. Finally, this paper revisits recent applications of DL algorithms in the field of food safety and authenticity detection, and prospects the challenges and future directions of DL algorithms in this field. Although DL has made many achievements in the field of food safety and authenticity detection, there is still a great potential for development. For example, the data augmentation function of GAN can assist CNN to obtain more training samples, thus improving the recognition rate. In addition, multimodal neural network (MNN) or multimodal attention network (MAN) can be also used to achieve the fusion of data from different modalities to further improve the robustness and accuracy of DL algorithms. [Display omitted] • Some popular DL algorithms in food safety and authenticity detection are introduced. • The improvements of DL algorithms to chemometric and ML algorithms are summarized. • Recent applications of DL algorithms in food safety and authenticity detection are reviewed. • Future prospects of DL algorithms in food safety and authenticity detection are discussed. [ABSTRACT FROM AUTHOR]

• Thiomalic acid modified CDTE was utilized for visual detection of Ag+. • High sensitivity and selectivity for Ag+were achieved. • The detection limit of fluorescence spectrometry method for Ag+ was 13.16 nmol/L. • Paper-based sensors for Ag+ was carried out in a rapid, simple and effective way. • PLSR accurately quantified concentrations of Ag+ in matrices based on paper images. A fluorescence paper-based sensor method by simply using thiomalic acid modified CdTe (TMA-capped CdTe) was proposed for visual detection of silver ions (Ag+). Red-emission TMA-capped CdTe with a large amount of carboxyl groups on the surface could be combined with Ag+ by electrostatic interaction, thus leading to the fluorescence quenching. It was found that the adding of amino acids contributed to fluorescence recovery by the leaving of Ag+ on the surface of TMA-capped CdTe. Moreover, the more amino group, benzene and thiol group equipped in the structure of amino acid, the stronger fluorescence restoring of CdTe could be achieved. This fluorescence spectrometry method for Ag+ showed low detection of limit with the value of 13.16 nmol/L, with no response to other heavy metals, which demonstrating its high sensitivity and high selectivity for Ag+. More importantly, this paper-based sensor method combined with chemometrics could accurately identify and quantify different concentrations of Ag+ in different complex matrices. This fast, economical, accurate and effective approach exhibited a new idea for the on-site detection of Ag+, and was expected to be extended to the on-site detection of Ag+ in biological and environmental fields. [ABSTRACT FROM AUTHOR]

Colorimetric sensor array is a sensitive, rapid, and inexpensive detection technology which simulates human olfaction system based on various organic dyes. In this work, a sensor array based on acid-sensitive CdTe QDs coupled with chemometrics method was developed and proved to be a rapid, accurate and sensitive method for identification of 32 kinds of Chinese traditional cereal vinegars (CTCV). The specificity of identification of this method was mainly depends on the organic acids and melanoidins of CTCV. Among them, organic acids can quench the fluorescence of QDs through enhancing their electron transfer (hydrogen bond) and resonance energy transfer, and the fluorescence intensity of melanoidin was closely related to the brewing technology and aging year of CTCV. The types and aging time of 32 CTCV can be 100% identified at a dilution of 1000 by partial least squares discriminant analysis, when the latent variables were 4. And only one kind of QDs is needed instead of various organic dyes to this kind of colorimetric sensor array. Except for vinegar, this method can also be used in the identification of other food which rich in organic acid. Image 1 • A colorimetric sensor array based on acids-sensitive CdTe QDs was developed. • This method can be used in the identification of the types and aging time of vinegar. • The specificity of identification was depends on the organic acids and melanoid. • Only one QDs is needed instead of various organic dyes to this sensor array. • This sensor array can also be used in other food which rich in organic acids. [ABSTRACT FROM AUTHOR]

[Display omitted] • The EEMF method was proposed for identification of LJF and LF and their geographical origin. • Identification discriminant models were developed using PCA-LDA, PLS-DA and RF. • All models achieved 100% accuracy in discriminating between LJF and LF species. • All models successfully predicted the different geographical origins of LJF and LF separately. Lonicerae japonicae flos (LJF) and Lonicerae flos (LF) are important traditional Chinese medicine with various effects and prescription compatibility. The accurate identification of LJF and LF and their geographical origin are of great significance to the quality control and correct medication. In this work, a simple, rapid and efficient strategy for identification of Lonicerae japonicae flos and Lonicerae flos and their geographical origin was proposed by combining excitation-emission matrix fluorescence (EEMF) with chemometrics. Excitation-emission matrix fluorescence (EEMF) spectra of LJF and LF samples were characterized by parallel factor analysis (PARAFAC) to acquire chemically meaningful information. Classification models were built using three chemometric methods, including partial least squares-discrimination analysis (PLS-DA), principal component analysis-linear discriminant analysis (PCA-LDA) and random forest (RF). These models were utilized to identify LJF and LF and their geographical origin. Results revealed that PCA-LDA model gained the optimal performance with 100% classification accuracy for distinguishing between LJF and LJF from different geographical origin. Therefore, the proposed strategy could be a competitive alternative for fast and accurate differentiation of LJF and LF and their geographical origin. [ABSTRACT FROM AUTHOR]

[Display omitted] • MS-based untargeted metabolomics was developed for varietal authentication of Chinese wines. • 32 and 61 metabolites in ESI+ and ESI– modes were tentatively identified as varietal markers. • KEGG analysis showed that indoles and flavonoids metabolisms are closely related to varieties. • OPLS-DA models based on varietal markers can achieve a total correct rate of 90.9 % or higher. Wine is a very popular alcoholic drink owing to its health benefits of antioxidant effects. However, profits-driven frauds of wine especially false declarations of variety frequently occurred in markets. In this work, an UHPLC-QTOF-MS-based untargeted metabolomics method was developed for metabolite profiling of 119 bottles of Chinese red wines from four varieties (Cabernet Sauvignon, Merlot, Cabernet Gernischt, and Pinot Noir). The metabolites of red wines from different varieties were assessed using orthogonal partial least-squares discriminant analysis (OPLS-DA) and analyzed using KEGG metabolic pathway analysis. Results showed that the differential compounds among different varieties of red wines are mainly flavonoids, phenols, indoles and amino acids. The KEGG metabolic pathway analysis showed that indoles metabolism and flavonoids metabolism are closely related to wine varieties. Based on the differential compounds, OPLS-DA models could identify external validation wine samples with a total correct rate of 90.9 % in positive ionization mode and 100 % in negative ionization mode. This study indicated that the developed untargeted metabolomics method based on UHPLC-QTOF-MS is a potential tool to identify the varieties of Chinese red wines. [ABSTRACT FROM AUTHOR]

Chrysanthemi Flos (Hangbaiju) is a traditional Chinese medicine and the third most popular beverage after Chinese tea and coffee, but there are geographical confusion and quality control problems in its market distribution. Here, we constructed an Fe3+-mediated fluorescence nanosensing system for the visual identification of Hangbaiju from different origins and the prediction of compounds content, which were verified by HPLC–HRMS. The formation of Fe3+@QDs was the main element of the method under electron transfer (ET) effects, as well as the presence of 1) chelation-quenched fluorescence (CHQF) effects, 2) homogeneous fluorescence resonance energy transfer (Homo-FRET), and 3) the TURN OFF/ON reaction pattern, between caffeic acid and its derivatives (CAs), Fe3+, QDs, flavonoids, and Fe3+@QDs, demonstrated by various characterization methods and density flooding theory (DFT). The visualisation appears green when the sample is high in CAs, red when high in flavonoids, and grey when low in both compounds. The random forest (RF) model based on spectral and visualisation data had an origin identification accuracy of 100 % and 93.33 %, respectively. Eight compounds content prediction based on RF models also showed good results. The best Rp and prediction errors of spectral and visual data were 0.97 and 0.95, 13 % and 12 %, respectively. [Display omitted] • A Fe3+-mediated visual sensor was constructed using quantum dots and chemometrics. • The sensor can predict the content of compounds and identify the origin of Hangbaiju. • Three recognition mechanisms among polyphenol, Fe3+, and sensor were demonstrated. • The accuracy of the origin identification based on visual data was 93.33 %. • The prediction error of the main compound content based on visual data was <17 %. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel 3D-fluorescence sensing based on HN-chitosan polymer probe was proposed for the first time. • The proposed sensing strategy was used to identify and quantify of potential adulteration in saffron. • PLS-DA and PCA-LDA models can achieve 100% accuracy for identification of saffron and adulteration saffron. • PLSR were constructed to quantitatively predict adulteration levels in saffron mixture. • The proposed strategy has great potentials in real-life authenticity detection. Saffron is a candidate for various kinds of fraud to make huge profits. The present study proposed an efficient three-dimensional (3D) fluorescence sensing strategy based on hydrophilic hydrazine-naphthalimide functionalized chitosan (HN-chitosan) polymer probe for rapid identification and quantification of potential adulteration in saffron. The amino functional group in the HN-chitosan probe reacted specifically with the Oxygen-containing group of active ingredients in saffron, amplifying the signal difference between saffron and the adulterants, which was comprehensively characterized by 3D fluorescence. Four advanced chemometrics methods were applied for the classification of saffron and adulterated saffron, and good performance were obtained in both training and prediction sets. Furthermore, the PLS regression model was applied to the prediction of adulteration level in saffron and showed satisfactory accuracy. This strategy provides a new solution for rapid identification and quantification of potential adulteration in saffron, which contributes to the healthy development of its industry. [ABSTRACT FROM AUTHOR]

[Display omitted] • 105 lipid molecules in coffee beans were identified. • The effect of roasting on the lipid of coffee beans was investigated. • Lipid profiles of coffee beans during roasting process were different significantly. • The early and late stages of roasting were crucial in the transformation of coffee lipid composition. Coffee is popular worldwide and its consumption is increasing in recent years. Although mass spectrometry-based lipidomics approaches have been prevalent, their application in studies related to detailed information and dynamic changes in lipid composition during coffee bean roasting is still limited. The aim of this study was to investigate the dynamic changes in coffee bean lipids during the roasting process. The lipid classes and lipid molecular species in coffee beans were characterized by lipidomic analysis combined with chemometrics. A total of 12 lipid classes and 105 lipid molecular species were identified and quantified. Triacylglycerols (TAG) was the most abundant lipid class in both green beans and roasted beans. The content of phosphatidylethanolamine (PE) and lysophosphatidylethanolamine (LPE) in green beans was obviously higher than that in roasted beans. Other phospholipids, such as phosphatidylinositol (PI), lysophosphatidylinositol (LPI), phosphatidylcholine (PC), lysophophatidylcholine (LPC) and phosphatidic acid (PA), showed a tendency to increase at the beginning of roasting, then decreased gradually. Several differential lipid molecule species, for instance, PE (16:0_18:2), PC (18:2_18:2) were significantly down-regulated, and PI (18:1_18:2) was significantly up-regulated. This study provided a scientific basis for the change of coffee bean lipids during the roasting process. [ABSTRACT FROM AUTHOR]

The flavor of high-temperature Daqu Baijiu with the same aroma type is similar but the quality and price are very different, so that the high-quality Baijiu is often pretended as low-quality Baijiu. In this study, excitation-emission matrix fluorescence spectrometry combined with chemometrics was proposed for rapid identification of high temperature Daqu Baijiu with the same aroma type. The method was based on seven components screened by alternating trilinear decomposition (ATLD), and the discriminant model was established by data-driven-soft independent modelling of class analogy (DD-SIMCA), with the discriminant rate of 100%. Some Maillard reaction products, such as pyrazine and furfural compounds, with correlation >0.8 were obtained by GC-MS combined with Pearson analysis, and the prediction accuracy between 90.32% and 96.77%. This method has the characteristics of high sensitivity, fast detection and no pre-treatment, which provides a new strategy for rapid discrimination of high-temperature Daqu Baijiu with the same aroma type. • A method was proposed for rapid identification of Baijiu with the same aroma type. • The method was based on 7 components resolved from three-dimensional fluorescence. • The accuracy of this discriminant model can reach 100%. • Fluorescence of 7 components is mainly related to Maillard reaction products (MRP). • Accuracy of these components in predicting the content of MRP is greater than 90%. [ABSTRACT FROM AUTHOR]

[Display omitted] • SWATH-MS based untargeted metabolomics was used to study QZT with different storage years. • After 5 years of storage, the chemical composition of QZT changed significantly. • 18 compounds were identified as critical metabolites responsible for QZT aging. • The chemical markers were verified by the peak area corresponding to MS2 ions. • A duration time prediction model was established based on chemical markers for QZT storage. It's generally believed that the longer the storage, the better the quality of dark tea, but the chemical differences of Qingzhuan tea (QZT) with different storage years is still unclear. Herein, in this work, an untargeted metabolomic approach based on SWATH-MS was established to investigate the differential compounds of QZT with 0–9 years' storage time. These QZT samples were roughly divided into two categories by principal component analysis (PCA). After orthogonal projections to latent structures discriminant analysis (OPLS-DA), 18 differential compounds were putatively identified as chemical markers for the storage year variation of QZT. Heatmap visualization showed that the contents of catechins, fatty acids, and some phenolic acids significantly reduced, flavonoid glycosides, triterpenoids, and 8-C N-ethyl-2-pyrrolidinone-substituted flavan-3-ols (EPSFs) increased with the increase of storage time. Furthermore, these chemical markers were verified by the peak areas corresponding to MS2 ions from SWATH-MS. Based on the extraction chromatographic peak areas of MS and MS2 ions, a duration time prediction model was built for QZT with correlation coefficient R2 of 0.9080 and 0.9701, and RMSEP value of 0.85 and 1.24, respectively. This study reveals the chemical differences of QZT with different storage years and provides a theoretical basis for the quality evaluation of stored dark tea. [ABSTRACT FROM AUTHOR]

Lilium bulbs (LB) are a highly nutritious food, and their quality and price are influenced by origin. The present study proposed a nano-effect excitation-emission matrix fluorescence (EEMF) combined with chemometrics strategy for rapidly identifying the geographical origin of LB. Nano-effect EEMF spectra of 280 LB samples from different origins were collected after reaction with bovine serum albumin-modified gold and silver nanoclusters (BSA-AuAgNCs). Further, partial least squares-discrimination analysis (PLS-DA) and principal component analysis-linear discriminant analysis (PCA-LDA) were used to establish classification models for identifying the geographical origin of LB based on the obtained nano-effect EEMF spectra. The result showed that PCA-LDA model gained the optimal performance, and the classification accuracy of the training set and the prediction set was 95.9% and 90.5%, respectively. The nano-effect EEMF spectra was based on the reaction of BSA-AuAgNCs with the components, such as phenolic acids, in LB through hydrogen bonding, which amplified the spectral difference. This study demonstrated that the proposed strategy is effective for identifying the geographical origins of LB, which provides a new idea for the geographic traceability of other foods. [Display omitted] • A novel nano-effect EEMF combined with chemometrics strategy was developed. • The proposed strategy was used for tracing the origin of Lilium bulbs. • The classification models were established by PLS-DA and PCA-LDA. • This work provides new insights for the geographic traceability of food. [ABSTRACT FROM AUTHOR]

Lilium brownii var. viridulum Baker is an economically important plant, and its bulbs are popular among consumers for their high nutritional value. Due to the influence of environment, the chemical composition of lily from different origin is different, which leads to the different medicinal and nutritional values. Thus, it is very significant and necessary to discriminate the origins of lily. In this study, lipid classes and molecular species of lily samples from eight origins were identified and quantified by UHPLC-QE-Orbitrap/MS/MS and combined with chemometrics for modeling and analysis. The results showed that a total of 16 lipid classes and 88 lipid molecules were identified and quantified. The partial least squares discriminant analysis (PLS-DA) showed that there were significant differences in lipid metabolites between LH (Daodi) and other origins. And 39 characteristic compounds such as TAG 16:0/18:0/18:2, TAG 18:0/18:1/18:2, and LDGCC 15:2 could be used as the markers to identify the geographical origin of lily. This study established a new method to identify the origin traceability of lily in different production areas, and provided a theoretical basis to evaluate the nutritional value of lilies from different origins. [Display omitted] • PI and PE are the main phospholipid components in lilii bulbus. • Lipid metabolomics can distinguish the geographical origin of lilii bulbus. • Thirty-nine characteristic components (VIP greater than 1) were obtained. • TAG compounds are the characteristic compounds to distinguish lilii bulbus origins. [ABSTRACT FROM AUTHOR]

• A sensitized ratio fluorescence probe based on CQDs and CdTe QDs was constructed. • The composite probe owned a tenfold increase in sensitivity to TCs than single QDs. • The composite probe could achieve visual detection of TCs. • Both single TCs and mixed multiple TCs could be recognized in actual samples. A ratiometric fluorescent probe based on N/S doped carbon dots (N/S-CQDs) and mercaptoacetic acid capped CdTe quantum dots (TGA-CdTe QDs) with sensitized and self-calibration functions was constructed to sensitively detect multiple tetracycline antibiotics (TCs). N/S-CQDs could attach stably to TGA-CdTe QDs and form a new composite ratiometric fluorescent probe that had a more than tenfold increase in sensitivity to TCs compared with each single QD. The probe could detect four common TCs as the color of the probe changed from bright red to dark red, and the limit of detection (LOD) was 1.47 × 10−2–1.78 × 10−2 mg/L. Practical applications of the probe in food and urine were also verified with recovery rates of 95.21%-104.97%. Due to the abundant spectral fingerprints provided by both QDs, this novel probe could accurately recognize not only different single TCs but also mixed TC samples even in actual samples combined with chemometrics. [ABSTRACT FROM AUTHOR]

[Display omitted] • A fluorescence sensor based on aggregation-caused quenching was designed. • The sensor exhibited sensitive and rapid detection to Cu2+ residues. • Colorimetric detection of Cu2+ is realized by capturing the change of fluorescence color. • This method has good application in quantifying Cu2+ in actual samples. Copper (Cu2+), as a heavy metal, accumulates in the human body to a certain extent, which can induce various diseases and endanger human health. Rapid and sensitive detection of Cu2+ is highly desired. In present work, a glutathione modified quantum dot (GSH-CdTe QDs) was synthesized and applied in a "turn-off" fluorescence probe to detect Cu2+. The fluorescence of GSH-CdTe QDs could be rapidly quenched in the presence of Cu2+ through aggregation-caused quenching (ACQ), resulting from the interaction between the surface functional groups of GSH-CdTe QDs and Cu2+ and the electrostatic attraction. In the range of 20–1100 nM, the Cu2+ concentration showed a good linear relationship with the fluorescence decline of the sensor, and the LOD is 10.12 nM, which was lower than the U.S. Environmental Protection Agency (EPA) defined limit (20 μM). Moreover, aiming to attain visual analysis, colorimetric method was also used for rapidly detecting Cu2+ by capturing the change in fluorescence color. Interestingly, the proposed approach has successfully been applied for the detection of Cu2+ in real samples (i.e., environment water, food and traditional Chinese medicine) with satisfactory results, which provides a promising strategy for the detection of Cu2+ in practical application with the merits of being rapid, simple and sensitive. [ABSTRACT FROM AUTHOR]

Lily bulbs have been historically used as an edible and medicinal homologous plant. Identifying the geographical origins of lily bulbs produced in specific origin is of great importance since the geographical origins of lily bulbs influence their quality and price greatly. In this work, an untargeted metabolomic method based on UHPLC-QTOF-MS was established for revealing the differential chemical constituents of lily bulbs among different origins and predicting the geographical origins of them by chemometric modeling. A total of 15 differential compounds were screened and identified from untargeted metabolomic data of 50 lily bulb samples by our previously developed AntDAS software. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) showed that samples from five different origins were obviously distinguished based on the differential compounds. What's more, 7 and 6 key characteristic markers were discovered by partial least squares-discriminant analysis (PLS-DA) and orthogonal partial least squares-discriminant analysis (OPLS-DA), respectively. Finally, heatmap, PLS-DA and OPLS-DA models were reconstructed based on the discovered key characteristic markers, and external validation lily bulb samples were successfully discriminated, with recognition rate of 100 %. This study demonstrated that the proposed strategy has great potentials for the differentiation and identification of the geographical origins of lily bulbs. • UHPLC-QTOF-MS-based untargeted metabolomic revealed differential compounds of lily bulbs. • A total of 15 differential compounds of lily bulbs were screened and identified by AntDAS. • Key characteristic markers for geographical origins traceability were discovered and applied. • PLS-DA and OPLS-DA models based on key characteristic markers can achieve 100 % accuracy. • The proposed strategy has great potentials for identifying origins of lily bulbs and other foods. [ABSTRACT FROM AUTHOR]

• Simultaneous quantitative method for volatile components in herbs was established. • The method only relied on the selected NIR-MIR spectra sections obtained by O-PLSDA. • Five terpenes and five alcohol compounds were screened as differential components. • Each terpene or alcohol had a unique variable weight combination during analysis. • 10 differential components were accurately quantified through PSO-VWLS-SVM model. A novel method based on GC–MS, near-infrared (NIR) and mid-infrared (MIR) spectroscopy combined with chemometrics was established to simultaneously analyze differential volatile components (DVCs) of herb samples. Herein, Florists Chrysanthemum was adopted as the representative sample. Through the introduction of Automatic data analysis workflow (AntDAS) and one-class partial least squares discriminant analysis (O-PLSDA) model, five kinds of terpenes and five kinds of alcohols were efficiently screened as DVCs. By using the selected NIR-MIR spectra sections combined with O-PLSDA, it could achieve the accurate identification of Florists Chrysanthemum from Chrysanthemum morifolium Ramat. What's more, since the selected spectra sections were closely related to the structural and content of DVCs, they could be further used for simultaneous quantitative analysis of DVCs combined with optimized variable-weighted least-squares support vector machine based on particle swarm optimization (PSO-VWLS-SVM). This method only adopted the same NIR-MIR sections for multiple component accurate quantification, highlighting its convenience. [ABSTRACT FROM AUTHOR]

Lilii bulbus is an important food in China with medicinal and edible functions, and its quality and price are closely related to its geographical origin. In this work, 4 stable isotopes (δ13C, δ15N, δ2H and δ18O) and 41 mineral elements of lilii bulbus samples from 11 origins and 4 species were collected and combined with chemometrics to identify origins and species. ANOVA and principal component analysis (PCA) showed significant differences (P < 0.05) in stable isotopes and multiple elements in lilii bulbus samples from different origins, and Cu, Zn, K, δ18O, Sr and Li are important contributors to the classification. Partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to classify different species of lilii bulbus with good classification performance, while indicating that K, Ca, Na and Mg elements could be used as effective elements for species classification. The external validation model of OPLS-DA showed high accuracy (>89%) for lilii bulbus species classification. This study shows that stable isotopes and multi-elemental combined chemometrics provide an effective method for lilii bulbus origin and species identification. [Display omitted] • Geographic identification of lilii bulbus with stable isotopes and elements is feasible. • δ18O are useful factors for identifying lilii bulbus's geographical origin. • K, Ca, Na and Mg are important factors to distinguish different species of lilii bulbus samples. • The accuracy of OPLS-DA in discriminating lilii bulbus species is more than 89.61%. [ABSTRACT FROM AUTHOR]

As a popular detection model, the fluorescence “turn-off” sensor based on quantum dots (QDs) has already been successfully employed in the detections of many materials, especially in the researches on the interactions between pesticides. However, the previous studies are mainly focused on simple single track or the comparison based on similar concentration of drugs. In this work, a new detection method based on the fluorescence “turn-off” model with water-soluble ZnCdSe and CdSe QDs simultaneously as the fluorescent probes is established to detect various pesticides. The fluorescence of the two QDs can be quenched by different pesticides with varying degrees, which leads to the differences in positions and intensities of two peaks. By combining with chemometrics methods, all the pesticides can be qualitative and quantitative respectively even in real samples with the limit of detection was 2 × 10 −8 mol L −1 and a recognition rate of 100%. This work is, to the best of our knowledge, the first report on the detection of pesticides based on the fluorescence quenching phenomenon of double quantum dots combined with chemometrics methods. What's more, the excellent selectivity of the system has been verified in different mediums such as mixed ion disruption, waste water, tea and water extraction liquid drugs. [ABSTRACT FROM AUTHOR]

• MS-based untargeted metabolomics was firstly proposed to identify origins of Chinese wines. • There was strong correlation between geographical origins and metabolic profiles of wines. • The differential metabolites were screened as markers by OPLS-DA with pairwise modeling. • Tentative identification of differential metabolites was conducted based on MS and MS2 data. • Reliable OPLS-DA models based on markers were constructed for predicting origins of wines. Identifying geographical origins of red wines made in specific regions is of significance since the false claim of geographical origins has been frequently exposed in China's wine industry. In this work, an untargeted metabolomic approach based on UPLC-QTOF-MS was established to discriminate geographical origins of Chinese red wines. The principal component analysis (PCA) showed significant differences between wine samples from three famous geographical origins in China. The metabolites contributing to the differentiation were screened by orthogonal partial least squares-discriminant analysis (OPLS-DA) with pairwise modeling. 40 and 46 differential metabolites in positive and negative ionization modes were putatively identified as chemical markers. Furthermore, heatmap visualization and OPLS-DA models were constructed based on these identified markers and external verification wine samples from different regions were successfully discriminated, with recognition rate up to 96.7%. This study indicated that UPLC-QTOF-MS-based untargeted metabolomics has great potential for the geographical origin traceability of Chinese red wines. [ABSTRACT FROM AUTHOR]

Heavy metal pollution has gradually become one of the most serious problems in global environmental pollution and can invade the environment, food, and medicine, eventually causing irreversible damage to the human body. Herein, we construct a multichannel visual fluorescence array sensor based on N-doped carbon dots (NCDs) and gold nanocluster (AuNCs). The sensor comprehensively considered the aggregation-induced enhanced fluorescence (AIE) effect of AuNCs, the electron transfer (ET) effect of NCDs, and the fluorescence resonance energy transfer (FRET) effect of AuNCs@NCDs. It was further combined with PLSDA and PLSR to realize rapid and accurate detection of multiple metal ions in tap water, traditional Chinese medicine (TCM), and soil. The LODs of Cd2+, Pb2+, and Hg2+ were 0.15 μmol/L, 0.20 μmol/L and 0.09 μmol/L, respectively. Simultaneously, we proposed an adjustable fluorescence spectral logic device to meet the different determination requirements towards heavy metals. The analysis result of metal ions can be directly achieved by the four output types, NOT (0, 0, 0), Cd2+ (1, 1, 0), Pb2+ (1, 0, 0) and Hg2+ (0, 0, 1). More interestingly, Cd2+ and Pb2+ can be removed by simple centrifugation or sedimentation, which can be observed through the Tyndall effect. Accordingly, we not only constructed the sensing method of multiple metal ions but also proposed a new strategy for future research on the migration of heavy metal ions from complex environments to TCM. • Develop a three-channel array sensing for the detection of Cd2+, Pb2+, and Hg2+. • Successful applied to visual detection of various heavy metals in complex samples. • A logic device was designed to quickly discriminate between different metal ions. • Cd2+ and Pb2+ can produce Tyndall effect with AuNCs and be removed by centrifugation. [ABSTRACT FROM AUTHOR]

[Display omitted] • EEM fluorescence combined with chemometrics was proposed for rapid identification the origin of Eucommia ulmoides for the first time. • PARAFAC were used for spectral characterization. • Classification model for the identification the geographical origin was established by kNN, PCA-LDA and PLS-DA. • Compared to other models, kNN model achieved satisfactory results. Eucommia ulmoides is an important and valuable traditional Chinese medicine with various medical functions, and has been widely used as health food in China, Japan, South Korea and other Asian countries for many years. The efficacy and quality of E. ulmoides are closely associated with the geographical origin. In this work, the potential of excitation-emission matrix (EEMs) fluorescence coupled with chemometric methods was investigated for simple, rapid and accurate for identification E. ulmoides from different geographical origins. Parallel factor analysis (PARAFAC) was applied for characterizing the fluorescence fingerprints of E. ulmoides samples. Moreover, k-nearest neighbor (kNN), principal component analysis-linear discriminant analysis (PCA-LDA) and partial least squares discriminant analysis (PLS-DA) models were used for the classification of E. ulmoides samples according to their geographical origins. The results showed that kNN model was more suitable for identification of E. ulmoides samples from different provinces. The kNN model could identify E. ulmoides samples from eight different geographical origins with 100% accuracy on the training and test sets. Therefore, the proposed method was available for conveniently and accurately determining the geographical origin of E. ulmoides , which can expect to be an attractive alternative method for identifying the geographic origin of other traditional Chinese medicines. [ABSTRACT FROM AUTHOR]

[Display omitted] • An innovative strategy based on nano-effect spectroscopy and data fusion is proposed. • The strategy achieved identification of cultivation method and years of Dendrobium huoshanense. • Mid-level fusion strategy based on nano-effects significantly improves discriminative accuracy. • PLS-DA and OPLS-DA achieved 100% accuracy of cultivation years and methods identification. • The cultivation years of Dendrobium huoshanense was satisfactorily achieved by PLSR model. Dendrobium huoshanense (DHS) is an expensive Chinese herb with medicinal and edible functions. The quality of DHS is closely related to cultivation methods and years. In this work, nano-effect near and mid infrared spectra of 320 DHS samples with four cultivation methods and four years were obtained, and the feature vectors were obtained using a mid-level data fusion strategy with significant variable extraction and integration using variable importance for the projection (VIP). The feature vectors then combined with partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) to identify the cultivation years and methods. The result showed that it is difficult to discriminate effectively by a single spectrum, and the accuracy of PLS-DA discrimination based on raw or simple fusion of raw spectra is only 95.5% and 84.4% or 99.6% and 88.5%, while the accuracy of discrimination based on nano-effect feature fusion spectra can be improved to 100%, and PLSR was used to quantify the cultivation year of the DHS with good linearity and accuracy. The nano-effect spectrum is based on the reaction of tetraphenyl zinc porphyrin (ZnTPP) with polysaccharides, flavonoids and alkaloids in DHS, which amplifies the spectral difference. This study demonstrates that multi-spectral feature vectors fusion method could potentially be a reliable analytical method to distinguish the cultivation methods and years in DHS herbs. [ABSTRACT FROM AUTHOR]

[Display omitted] • Comparing traditional near-infrared spectroscopy, nano-effect near-infrared spectroscopy obtains superior classification performance. • Near-infrared spectroscopy sensor was developed for identification of the geographical origin of lily for the first time. • PLS-DA established a model to distinguish the geographical origin of lily. Near-infrared spectroscopy technique is a prevailing tool for quality control of foods and traditional Chinese medicines. However, it usually faced the problems of severe peak overlap, low classification accuracy and poor specificity. In this work, the potential of carbon dot-tetramethoxyporphyrin nanocomposite-based nano-effect near-infrared spectroscopy sensor combined with chemometric method was investigated for the accurate identification lily from different geographical origins. Partial least squares-discriminant analysis (PLS-DA) was used for differentiating geographical origins of lily based on the collected traditional and nano-effect near-infrared spectroscopy. Compared with traditional near-infrared spectroscopy, the nano-effect near-infrared spectroscopy obtains superior classification performance with 100% accuracy on the training and test set. The results showed that the proposed method based on near-infrared spectroscopy combined with nanocomposites and chemometrics could be considered as a promising tool for rapid discrimination of the authenticity of food and traditional Chinese medicine in the future. [ABSTRACT FROM AUTHOR]

• GO-TAP-based mid-infrared spectroscopy for Pu-erh teas analysis was developed. • Nano-effect MIR spectra method was superior to the traditional MIR spectra method. • Twelve categories of Pu-erh teas were successfully classified by PLSDA model. • The age prediction of Pu-erh teas was satisfactorily achieved by PLSR model. Pu-erh tea is a unique post-fermented tea with special quality and unique aroma characteristics, which are achieved by aging as part of the tea-making processing in China. In this work, we developed a new method, based on the combination of the mid-infrared (MIR) spectroscopy of graphene oxide-5,10,15,20-mesotetra (4-aminophenyl) porphyrin hybrid (GO-TAP), with chemometrics, for the variety identification and age prediction of twelve Pu-erh teas. Partial least squares discriminant analysis (PLSDA) and partial least squares regression (PLSR) models were respectively employed to classify the variety of and predict the age of Pu-erh teas based on the collected MIR spectra. Compared with traditional MIR spectroscopy, MIR spectroscopy based on GO-TAP gave better accuracy and specificity in variety classification and age prediction. The GO-TAP complex-based MIR spectroscopy sensor coupled with chemometrics could achieve 100% accuracy for variety classification and predicts storage time of Pu-erh tea. This method can be applied for the accurate identification and prediction of Pu-erh tea storage time, and it might have enormous potential for the application in storage time identification of other food stuff. [ABSTRACT FROM AUTHOR]

• A direct and effective method for the carbamate pesticides detection was developed. • The LODs of our methods are comparable with traditional chromatographic techniques. • Our methods have high sensitivity and selectivity, even in complex food matrices. • Carbamate pesticides detection could be achieved within 5 min by paper sensors. The needing of rapid and sensitive detection method for pesticides is increasing, to facilitate its detection without complicated instruments. Herein, a novel paper-based senor was developed for the visual detection of three carbamate pesticides (metolcarb, carbofuran, and carbaryl) based on CdTe quantum dots (QDs) and nano zinc 5, 10, 15, 20-tetra(4-pyridyl)-21 H -23 H -porphine (nano ZnTPyP) with a "turn-off-on" mode. This fluorescence sensing model could be applied in the highly selective and sensitive detection of carbamate pesticides both by fluorescence spectrometry or paper-based sensors. Based on the extracted RGB color values of paper, the partial least squares regression (PLSR) was used to accurately quantify the concentrations of carbamate pesticides in different food matrices (apple, cabbage and tea water). This method featured in high speed, low price and high accuracy, and provided a new strategy for the detection of food safety. [ABSTRACT FROM AUTHOR]