Rapid and practical qualitative and quantitative evaluation of non-fumigated ginger and sulfur-fumigated ginger via Fourier-transform infrared spectroscopy and chemometric methods

Rapid prediction of the chemical composition of pet food using a benchtop and handheld near-infrared spectrometer

Quality of pet foods can be affected by many factors such as raw materials, formulations, and processing techniques. The pet food manufacturers require fast analyses to control the nutritional quality of their products. Herein, near-infrared spectroscopy (NIR) was evaluated to quantify the chemical composition of pet food, and the performances of two NIR spectrometers were investigated and compared: a benchtop instrument (1000-2500 nm) and a low-cost handheld instrument (900-1700 nm). Seventy cat food and thirty-six dog samples were characterized using reference methods for crude protein, crude fat, crude fibre, crude ash, moisture, calcium (Ca), and phosphorus (P). Principal component regression (PCR) and partial least squares regression (PLSR) were used to establish the models that involved the cat food and mixed model. The characteristic wavelengths were selected using a competitive adaptive reweighted-sampling (CARS) algorithm. The Optimal models obtained from the benchtop instrument for crude protein, crude fat, and moisture were classified as "Good" or "Very good" (Residual prediction variation (RPD) > 3), for crude fibre were classified as "Poor" (RPD>2), and for crude ash, Ca and P (RPD<2) were classified as "Very poor". The Optimal calibrations obtained from the handheld instrument for crude protein, crude fat, and moisture were classified as "Good" or "Very good" (RPD>3), for crude fibre, crude ash, Ca, and P were classified as "Very poor" (RPD<2). Generally, the the performance of benchtop and handheld instrument was close, and the cat food model outperformed the mixed model. Results from the current study revealed the potential to monitor the chemical compositions in pet food on a large scale.

An efficient method for rapid screening of triterpenoid saponins in three Glycyrrhiza species using rapid resolution liquid chromatography quadrupole time-of-flight mass spectrometry combined with mass defect filtering

Triterpenoid saponins, a major bioactive component of liquorice, possess high hydrophilicity and often co-occur with other impurities of similar polarity. Additionally, subtle structural differences of some triterpenoid saponins bring challenges to comprehensive characterisation. In this study, triterpenoid saponins of three Glycyrrhiza species were systematically analysed using rapid resolution liquid chromatography quadrupole time-of-flight mass spectrometry (RRLC-Q-TOF-MS) coupled with mass defect filtering (MDF). Firstly, comprehensive date acquisition was achieved using RRLC-Q-TOF-MS. Secondly, a polygonal MDF method was established by summarizing known and speculated substituents and modifications based on the core structure to rapidly screen potential triterpenoid saponins. Thirdly, based on the fragmentation patterns of reference compounds, an identification strategy for characterisation of triterpenoid saponins was proposed. The strategy divided triterpenoid saponins into three distinct classes. By this strategy, 98 triterpenoid saponins including 10 potential new ones were tentatively characterised. Finally, triterpenoid saponins of three Glycyrrhiza species were further analysed using principle component analysis (PCA) and orthogonality partial least squares discriminant analysis (OPLS-DA). Among these, 18 compounds with variable importance in projections (VIP) > 1.0 and P values < 0.05 were selected to distinguish three Glycyrrhiza species. Overall, our study provided a reference for quality control and rational use of the three species.

Rapid quality assessment and traceability of ginger powder from Northeast India and Indian market based on near infrared spectroscopic fingerprinting

INTRODUCTION

Ginger (Zingiber officinale Rosc.) varies widely due to varying concentrations of phytochemicals and geographical origin. Rapid non-invasive quality and traceability assessment techniques ensure a sustainable value chain.

OBJECTIVE

The objective of this study is the development of suitable machine learning models to estimate the concentration of 6-gingerol and check traceability based on the spectral fingerprints of dried ginger samples collected from Northeast India and the Indian market using near-infrared spectrometry.

METHODS

Samples from the market and Northeast India underwent High Performance Liquid Chromatographic analysis for 6-gingerol content estimation. Near infrared (NIR) Spectrometer acquired spectral data. Quality prediction utilized partial least square regression (PLSR), while fingerprint-based traceability identification employed principal component analysis and t-distributed stochastic neighbor embedding (t-SNE). Model performance was assessed using RMSE and R2 values across selective wavelengths and spectral fingerprints.

RESULTS

The standard normal variate pretreated spectral data over the wavelength region of 1,100-1,250 nm and 1,325-1,550 nm showed the optimal calibration model with root mean square error of calibration and R2 C (coefficient of determination for calibration) values of 0.87 and 0.897 respectively. A lower value (0.24) of root mean square error of prediction and a higher value (0.973) of R2 P (coefficient of determination for prediction) indicated the effectiveness of the developed model. t-SNE performed better clustering of samples based on geographical location, which was independent of gingerol content.

CONCLUSION

The developed NIR spectroscopic model for Indian ginger samples predicts the 6-gingerol content and provides geographical traceability-based identification to ensure a sustainable value chain, which can promote efficiency, cost-effectiveness, consumer confidence, sustainable sourcing, traceability, and data-driven decision-making.

Rapid quality evaluation and geographical origin recognition of ginger powder by portable NIRS in tandem with chemometrics

Ginger powder is an important spice that is susceptible to improper sales such as adulteration or geographical fraud. In this study, a portable near infrared spectroscopy was used to quantitatively predict the 6-gingerol content, an important quality index of ginger, as well as to identify the gingers from three origins in China. Specifically, the optimal preprocessing method was first investigated by comparing the predictions of models. Then three feature variable selection methods including PCA, CARS, and RFrog, on the quantitative analysis of 6-gingerol were also compared, respectively. After comparison, the PLS model established on the S-G combined with SNV preprocessing outperformed the others. The PLS regression of 6-gingerol with variables selected by RFrog possessed the Rc2 of 0.9463, Rp2 of 0.9497, and the RPD of 4.2257, respectively. Moreover, the results further verified that the LDA model by SPA variables extraction successfully identify gingers from different origins with 100 % accuracy.

Improved quantitative analysis of tenuifolin using hydrolytic continuous-flow system to build prediction models for its content based on near-infrared spectroscopy

This study used two types of analyses and statistical calculations on powdered samples of Polygala root (PR) and Senega root (SR): (1) determination of saponin content by an independently developed quantitative analysis of tenuifolin content using a flow reactor, and (2) near-infrared spectroscopy (NIR) using crude drug powders as direct samples for metabolic profiling. Furthermore, a prediction model for tenuifolin content was developed and validated using multivariate analysis based on the results of (1) and (2). The goal of this study was to develop a rapid analytical method utilizing the saponin content and explore the possibility of quality control through a wide-area survey of crude drugs using NIR spectroscopy. Consequently, various parameters and appropriate wavelengths were examined in the regression analysis, and a model with a reasonable contribution rate and prediction accuracy was successfully developed. In this case, the wavenumber contributing to the model was consistent with that of tenuifolin, confirming that this model was based on saponin content. In this series of analyses, we have succeeded in developing a model that can quickly estimate saponin content without post-processing and have demonstrated a brief way to perform quality control of crude drugs in the clinical field and on the market.

Gingerols and shogaols: A multi-faceted review of their extraction, formulation, and analysis in drugs and biofluids to maximize their nutraceutical and pharmaceutical applications

Gingerols represent the main bioactive compounds in ginger drugs mostly Zinigiber officinale (F. Zingebraceae) and account for the biological activities and the strong/pungent flavor in ginger. Ginger (Z. officinale) rhizome is one of the most valued herbal drugs for ailments' treatment in many ayurvedic medicine asides from its culinary applications as a spice. Gingerols and their dehydrated products shogaols are phenolic phytochemicals found in members of the Zingiberaceae family and account for most of their effects including anti-inflammatory and anticancer activities. This review entails most of the novel trends related to the extraction, optimization, and formulations of gingerols and shogaols to insure best recoveries and efficacies from their natural resources. Further, it presents a comprehensive overview of the different analytical approaches for the determination of gingerols/shogaols' levels in nutraceuticals to ensure highest quality and for their detection in body fluids for proof of efficacy.

Combining stable isotope, multielement and untargeted metabolomics with chemometrics to discriminate the geographical origins of ginger (Zingiber officinale Roscoe)

Ginger (Zingiber officinale Roscoe) is a high-value food and herb worldwide. The quality of ginger is often related to its production regions. In this study, stable isotopes, multiple elements, and metabolites were investigated together to realize ginger origin traceability. Chemometrics showed that ginger samples could be preliminarily separated, and 4 isotopes (δ13C, δ2H, δ18O, and δ34S), 12 mineral elements (Rb, Mn, V, Na, Sm, K, Ga, Cd, Al, Ti, Mg, and Li), 1 bioelement (%C), and 143 metabolites were the most important variables for discrimination. Furthermore, three algorithms were introduced, and the fused dataset based on VIP features led to the highest accuracies for origin classification, with predictive rates of 98% for K-nearest neighbor and 100% for support vector machine and random forest. The results demonstrated that isotopic, elemental, and metabolic fingerprints were useful indicators for the geographical origins of Chinese ginger.

Impacts of sulfur fumigation on the chemistry and immunomodulatory activity of polysaccharides in ginseng

Ginseng is widely regarded as a panacea in Oriental medicine mainly due to its immunomodulatory activity. We previously found that sulfur fumigation, a commonly used pesticidal and anti-bacterial processing practice, weakened the immunomodulatory activity of ginseng. However, if and how sulfur fumigation affects the polysaccharides in ginseng, the crucial components contributing to the immunomodulatory function, remain unknown. Here we report that polysaccharides extracted from sulfur-fumigated ginseng (SGP) presented different chemical properties with polysaccharides extracted with non-fumigated ginseng (NGP), particularly increased water extraction yield and decreased branching degree. SGP had weaker immunomodulatory activity than NGP in immunocompromised mice, as evidenced by less improved immunophenotypes involving body weight, immune organ indexes, white blood cells, lymphocyte cell populations and inflammation. The different immunomodulatory activities were accompanied by changes in the interaction between the polysaccharides and gut microbiota, in which SGP stimulated the growth of different bacteria but produced less SCFAs as compared to NGP. Fecal microbiota transplantation experiment suggested that gut microbiota played a central role in causing the weakened immunomodulatory activity in vivo. This study provides definite evidence that sulfur fumigation affects the chemistry and bioactivity of ginseng polysaccharides, thereby contributing to understanding how sulfur fumigation weakens the immunomodulatory activity of ginseng.

The Use of Infrared Spectroscopy for the Quantification of Bioactive Compounds in Food: A Review

Infrared spectroscopy (wavelengths ranging from 750-25,000 nm) offers a rapid means of assessing the chemical composition of a wide range of sample types, both for qualitative and quantitative analyses. Its use in the food industry has increased significantly over the past five decades and it is now an accepted analytical technique for the routine analysis of certain analytes. Furthermore, it is commonly used for routine screening and quality control purposes in numerous industry settings, albeit not typically for the analysis of bioactive compounds. Using the Scopus database, a systematic search of literature of the five years between 2016 and 2020 identified 45 studies using near-infrared and 17 studies using mid-infrared spectroscopy for the quantification of bioactive compounds in food products. The most common bioactive compounds assessed were polyphenols, anthocyanins, carotenoids and ascorbic acid. Numerous factors affect the accuracy of the developed model, including the analyte class and concentration, matrix type, instrument geometry, wavelength selection and spectral processing/pre-processing methods. Additionally, only a few studies were validated on independently sourced samples. Nevertheless, the results demonstrate some promise of infrared spectroscopy for the rapid estimation of a wide range of bioactive compounds in food matrices.

Rapid evaluation of Radix Paeoniae Alba and its processed products by near-infrared spectroscopy combined with multivariate algorithms

It is well known that the processing method of herbal medicine has a complex impact on the active components and clinical efficacy, which is difficult to measure. As a representative herb medicine with diverse processing methods, Radix Paeoniae Alba (RPA) and its processed products differ greatly in clinical efficacy. However, in some cases, different processed products are confused for use in clinical practice. Therefore, it is necessary to strictly control the quality of RPA and its processed products. Giving that the time-consuming and laborious operation of traditional quality control methods, a comprehensive strategy of near-infrared (NIR) spectroscopy combined with multivariate algorithms was proposed. This strategy has the advantages of being rapid and non-destructive, not only qualitatively distinguishing RPA and various processed products but also enabling quantitative prediction of five bioactive components. Qualitatively, the subspace clustering algorithm successfully differentiated RPA and three processed products, with an accuracy rate of 97.1%; quantitatively, interval combination optimization (ICO), competitive adaptive reweighted sampling (CARS), and competitive adaptive reweighted sampling combined with successive projections algorithm (CARS-SPA) were used to optimize the PLS model, and satisfactory results were obtained in terms of wavelength selection. In conclusion, it is feasible to use NIR spectroscopy to rapidly evaluate the effect of processing methods on the quality of RPA, which provides a meaningful reference for quality control of other herbal medicines with numerous processing methods.

Trends in digital detection for the quality and safety of herbs using infrared and Raman spectroscopy

Herbs have been used as natural remedies for disease treatment, prevention, and health care. Some herbs with functional properties are also used as food or food additives for culinary purposes. The quality and safety inspection of herbs are influenced by various factors, which need to be assessed in each operation across the whole process of herb production. Traditional analysis methods are time-consuming and laborious, without quick response, which limits industry development and digital detection. Considering the efficiency and accuracy, faster, cheaper, and more environment-friendly techniques are highly needed to complement or replace the conventional chemical analysis methods. Infrared (IR) and Raman spectroscopy techniques have been applied to the quality control and safety inspection of herbs during the last several decades. In this paper, we generalize the current application using IR and Raman spectroscopy techniques across the whole process, from raw materials to patent herbal products. The challenges and remarks were proposed in the end, which serve as references for improving herb detection based on IR and Raman spectroscopy techniques. Meanwhile, make a path to driving intelligence and automation of herb products factories.

Rapid detection and quality evaluation of Shuang-Huang-Lian injection by ATR-IR and NIR spectroscopy combined with chemometrics

Quality evaluation and consistency evaluation of drugs are the keys to ensure the therapeutic effect and safety of drugs. In this study, attenuated total refraction infrared (ATR-IR) spectroscopy and near-infrared (NIR) spectroscopy combined with chemometrics were used for rapid detection and quality evaluation of active components of Shuang-Huang-Lian injection (SHLI), a traditional Chinese medicine preparation commonly used in China. Taking the chromatographic detection results as a reference, the partial least squares (PLS) model based on ATR-IR and NIR data was constructed by removing the bands with serious noise interference and low signal frequency band. The results showed that the PLS model achieved satisfactory results for the prediction of the three active components (chlorogenic acid, baicalin and phillyrin) in SHLI, indicating that the two spectral techniques combined with the PLS regression method could be successfully used for rapid quantitative analysis of the three active ingredients in SHLI. Relatively, the PLS model based on the ATR-IR spectrum has higher R² and smaller RMSE than it based on the NIR spectrum. Furthermore, based on the rapid quantitative analysis of the three active ingredients in SHLI, the quality of 140 SHLI samples from seven manufacturers was evaluated by TOPSIS (technique for order preference by similarity to the ideal solution) analysis, and the consistency of different batches of SHLI products from the same manufacturer was evaluated. The results showed that there were differences in the quality of SHLI produced by different manufacturers, and the quality of different batches of SHLI produced by the same manufacturer was not completely consistent. In conclusion, ATR-IR and NIR spectroscopy combined with chemometrics can be used for accurate and rapid quantitative analysis and quality evaluation of SHLI. This study provides a good idea for the rapid quantitative analysis and quality evaluation of drugs or food based on spectroscopic technology and chemometrics.

Study on the Correlation Between the Appearance Traits and Intrinsic Chemical Quality of Bitter Almonds Based on Fingerprint-Chemometrics

Bitter almond is a well-known and commonly used traditional Chinese medicine (TCM) for relieving coughs and asthma. However, the bioactive chemical composition of bitter almonds, especially their amygdalin content, which determines their quality for TCM use, is variable and this can cause problems with formulating and prescribing TCMs based on bitter almonds. Therefore, a simple method was developed to evaluate the compositional quality of bitter almonds from their appearance traits, based on a combination of chromatographic fingerprinting and chemometrics. Bitter almonds were analyzed by high-performance liquid chromatography (HPLC). Hierarchical cluster analysis (HCA) and principal components analysis (PCA) were applied to classify bitter almonds, which split the samples into two independent clusters. Three chemical markers (amygdalin, prunasin, and one unidentified component) were found by partial least squares-discriminant analysis (PLS-DA). What's more, a new PLS-DA model was reconstructed to confirm the obtained chemical markers from PLS-DA. Additionally, the appearance trait indices and amygdalin content of bitter almond were determined and the classification was confirmed by one-way analysis of variance. This method can easily determine the quality of bitter almonds from their appearance alone, high quality correlated closely with kernels that were larger, oblong in shape and heavier.

Headspace GC/MS and fast GC e-nose combined with chemometric analysis to identify the varieties and geographical origins of ginger (Zingiber officinale Roscoe)

Food authenticity regarding different varieties and geographical origins is increasingly becoming a concern for consumers. In this study, headspace gas chromatography-mass spectrometry (HS-GC-MS) and fast gas chromatography electronic nose (fast GC e-nose) were used to successfully distinguish the varieties and geographical origins of dried gingers from seven major production areas in China. By chemometric analysis, a distinct separation between the two varieties of ginger was achieved based on HS-GC-MS. Furthermore, flavor information extracted by fast GC e-nose realized the discrimination of geographical origins, and some potential flavor components were selected as important factors for origin certification. Moreover, several pattern recognition algorithms were compared in varietal and regional identification, and random forest (RF) led to the highest accuracies for discrimination. Overall, a rapid and precise method combining multivariate chemometrics and algorithms was developed to determine varieties and geographical origins of ginger, and it could also be applied to other agricultural products.

Impact of Sulfur Fumigation on Ginger: Chemical and Biological Evidence

We previously found that sulfur fumigation, a commonly used controversial method for the post-harvest handling of ginger, induces the generation of a compound in ginger, which was speculated to be a sulfur-containing derivative of 6-shogaol based on its mass data. However, the chemical and biological properties of the compound remain unknown. As a follow-up study, here we report the chemical structure, systemic exposure, and anticancer activity of the compound. Chromatographic separation, nuclear magnetic resonance analysis, and chemical synthesis structurally elucidated the compound as 6-gingesulfonic acid. Pharmacokinetics in rats found that 6-gingesulfonic acid was more slowly absorbed and eliminated, with more prototypes existing in the blood than 6-shogaol. Metabolism profiling indicated that the two compounds produced qualitatively and quantitatively different metabolites. It was further found that 6-gingesulfonic acid exerted significantly weaker antiproliferative activity on tumor cells than 6-shogaol. The data provide chemical and biological evidence that sulfur fumigation may impair the healthcare functions of ginger.

Rapid detection of adulteration in powder of ginger (Zingiber officinale Roscoe) by FT-NIR spectroscopy combined with chemometrics

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Rapid detection of adulteration in GP was realized by NIR spectroscopy.

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PCA and PLS-DA models were successfully explored to identify adulterants in GP.

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Three algorithms achieved satisfactory results for discrimination of adulteration.

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Adulteration levels in GP can be predicted by PLSR model.

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The optimal pretreatment methods were compared and selected for modeling.

Ginger powder (GP) is a popular spice in the world. Duo to its nutritional value, GP is regarded as an attractive target for adulteration, which is not easily detected. In this study, chromaticity analysis and Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometrics were developed to identify and quantify of GP and its adulterants. The result showed that GPs and adulterated GPs cannot be completely distinguished by chromaticity analysis. While, the optimized NIR spectra could accurately distinguish the authentic GPs from those adulterated samples. Random forest and gradient boosting algorithms exhibited the highest accuracies (100%) in classification. Moreover, a quantitative model was successfully established to predict the adulteration level in GP. The optimal parameters of prediction to deviation were 8.92, 13.68, 14.61, and 4.30, for pure and adulterated GPs. Overall, FT-NIR spectroscopy is a promising tool, which can quickly identify potential adulteration in GP and track the types of adulterants.

Rapid Detection of Available Nitrogen in Soil by Surface-Enhanced Raman Spectroscopy

Soil-available nitrogen is the main nitrogen source that plants can directly absorb for assimilation. It is of great significance to detect the concentration of soil-available nitrogen in a simple, rapid and reliable method, which is beneficial to guiding agricultural production activities. This study confirmed that Raman spectroscopy is one such approach, especially after surface enhancement; its spectral response is more sensitive. Here, we collected three types of soils (chernozem, loess and laterite) and purchased two kinds of nitrogen fertilizers (ammonium sulfate and sodium nitrate) to determine ammonium nitrogen (NH4-N) and nitrate nitrogen (NO3-N) in the soil. The spectral data were acquired using a portable Raman spectrometer. Unique Raman characteristic peaks of NH4-N and NO3-N in different soils were found at 978 cm−1 and 1044 cm−1, respectively. Meanwhile, it was found that the enhancement of the Raman spectra by silver nanoparticles (AgNPs) was greater than that of gold nanoparticles (AuNPs). Combined with soil characteristics and nitrogen concentrations, Raman peak data were analyzed by multiple linear regression. The coefficient of determination for the validation (Rp2) of multiple linear regression prediction models for NH4-N and NO3-N were 0.976 and 0.937, respectively, which deeply interpreted the quantitative relationship among related physical quantities. Furthermore, all spectral data in the range of 400–2000 cm−1 were used to establish the partial least squares (PLS), back-propagation neural network (BPNN) and least squares support vector machine (LSSVM) models for quantification. After cross-validation and comparative analysis, the results showed that LSSVM optimized by particle swarm methodology had the highest accuracy and stability from an overall perspective. For all datasets of particle swarm optimization LSSVM (PSO-LSSVM), the Rp2 was above 0.99, the root mean square errors of prediction (RMSEP) were below 0.15, and the relative prediction deviation (RPD) was above 10. The ultra-portable Raman spectrometer, in combination with scatter-enhanced materials and machine learning algorithms, could be a promising solution for high-efficiency and real-time field detection of soil-available nitrogen.

Multi-wavelength fusion column fingerprint technology combined with chemometric analysis to evaluate the overall quality of the Gardenia jasminoides root

Chromatographic fingerprinting provides effective technical means for quality evaluation of traditional Chinese medicine. In this work, a novel multi-wavelength fusion column fingerprint was obtained by intelligent selection of chromatographic peaks from different wavelengths, which displayed the maximum peak area information under the optimal wavelength at the same retention time. Here, the Gardenia jasminoides root was selected as a sample. The multi-wavelength fusion column fingerprint graph of the Gardenia jasminoides root was constructed from five wavelengths (203 nm, 210 nm, 238 nm, 250 nm and 330 nm). The peak capacity, peak resolution, the number of common peaks and similarity were used to evaluate the performance. The 19 batches of Gardenia jasminoides root were classified into three categories with clear distinction between origin categories based on the multi-wavelength fusion column fingerprint combined with chemometrics, including hierarchical cluster analysis and principal component analysis. Nine markers of variation that led to differences between batches were screened by orthogonal partial least squares discriminant analysis. This study demonstrated that the classification model based on the multi-wavelength fusion column fingerprint was better than that on a single-wavelength, and the fusion fingerprint was suitable for the identification and quality control of traditional Chinese medicine with more comprehensive chemical composition information and more accurate prediction ability.

Quality control of Zingiberis Rhizoma and its processed products by UHPLC-Q-TOF/MS-based non-targeted metabonomics combining with SIBDV method

Zingiberis Rhizoma (ZR) is a homologous plant with pungent tastes and aromas, which has unique nutritional value and tremendous application potentiality. Zingiberis Rhizoma Praeparatum (ZRP) and Carbonised Ginger (CG) are processed products of ZR through different processing methods, and they are commonly used ingredients in food supplements. This study used ZR, ZRP and CG from different batches to further understand composition differences after processing. Additionally, we performed non-targeted metabolomics-based profiling of gingerols by ultra-high-performance liquid chromatography coupled with hybrid triple quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS) in combination with multivariate analysis and compounds identification. In which, we developed a comprehensive SWATH-IDA bi-directionally verified (SIBDV) method integrating the advantages of Sequential Windowed Acquisition of all Theoretical fragment ions (SWATHTM) and traditional information-dependent acquisition (IDA) mode for characterization of gingerols. Potential chemical markers were selected by principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) of chemometrics methods. After that, the threshold variable importance in projection (VIP) value and P value were employed to screen the valuable MS features for discriminating ZR, ZRP and CG. In total, 59 gingerols in the different samples were structurally identified. Results allowed the selection of 33 gingerols, which are nominated as novel markers for materials authentication in ZR, ZRP and CG. The analysis of the study showed that the content of gingerols showed a downward trend after processing, but shogaols and gingerone compounds had an upward trend, resulting in differences in application and pharmacodynamic efficacy. These findings provide promising perspectives in the quality control of ZR, ZRP and CG, as well as for laying the foundation in food design and development.

A Rapid Non-Destructive Hyperspectral Imaging Data Model for the Prediction of Pungent Constituents in Dried Ginger

Ginger is best known for its aromatic odour, spicy flavour and health-benefiting properties. Its flavour is derived primarily from two compound classes (gingerols and shogaols), with the overall quality of the product depending on the interaction between these compounds. Consequently, a robust method for determining the ratio of these compounds would be beneficial for quality control purposes. This study investigated the feasibility of using hyperspectral imaging to rapidly determine the ratio of 6-gingerol to 6-shogoal in dried ginger powder. Furthermore, the performance of several pre-processing methods and two multivariate models was explored. The best-performing models used partial least squares regression (PSLR) and least absolute shrinkage and selection operator (LASSO), using multiplicative scatter correction (MSC) and second derivative Savitzky–Golay (2D-SG) pre-processing. Using the full range of wavelengths (~400–1000 nm), the performance was similar for PLSR (R² ≥ 0.73, RMSE ≤ 0.29, and RPD ≥ 1.92) and LASSO models (R² ≥ 0.73, RMSE ≤ 0.29, and RPD ≥ 1.94). These results suggest that hyperspectral imaging combined with chemometric modelling may potentially be used as a rapid, non-destructive method for the prediction of gingerol-to-shogaol ratios in powdered ginger samples.

A gradient-based discriminant analysis method for process quality control of carbonized TCM via Fourier transform near infrared spectroscopy: A case study on carbonized Typhae Pollen

Carbonized traditional Chinese medicine (TCM) is a kind of distinctive traditional drug which has been widely used in various bleeding syndromes for over two thousand years, and most of them are still in clinical use. Although they share similar processing method: stir-frying, there are no specific quality standards and few quality control researches carried out on carbonized TCM up until now. Carbonized Typhae Pollen (CTP) is a typical carbonized TCM with efficacy of eliminating blood stasis and stanching bleeding. In this study, a novel process quality control model coupled with near infrared spectroscopy was established, called Gradient-based Discriminant Analysis method (GDA). Compared with conventional modeling methods (Convolutional Neural Network, Linear Discriminant Analysis, Standard Normal Variate-LDA), GDA model applied in fiber optic probe acquisition mode exhibited highest test accuracy (0.961), satisfactory correct identification (internal validation, 100%; external validation, 97.1%) and excellent model stability. This method provided a perfect guideline for process quality control of Carbonized TCM as well as ensured their clinical efficacy.

Detection and discrimination of sulfur dioxide using a colorimetric sensor array

Discrimination and detection of sulfur dioxide residues in foods using a simple colorimetric array have been achieved. The difference maps before and after the reaction showed that the specific color fingerprint was related to the amount of sulfur dioxide. The results of principal component analysis (PCA), hierarchical clustering analysis (HCA) and linear discriminant analysis (LDA) demonstrated that the as-fabricated colorimetric sensor array have good performance for the discrimination of sulfur dioxide and other interferents, as well as different concentrations of sulfur dioxide. Moreover, the array has been successfully applied to determine the concentration of sulfur dioxide residues in real samples and revealed good accuracy, precision and repeatability.

In this work, a colorimetric sensor array based on six specific color reactions was developed and used for the determination of sulfur dioxide content. The qualitative and quantitative analysis of sulfur dioxide residues in real samples was achieved.

Assessing the Quality of Calyx of Physalis alkekengi L. var. franchetii Based on Quantitative Analysis of Q-Marker Combined with Chemometrics and Machine Learning Algorithms

Physalis alkekengi L. var. franchetii (PALF) is a traditional Chinese medicine, which is well known for its antimicrobial, anti-inflammatory, antipyretic, and expectorant properties. Its fruits and fruiting calyxes are used as dietary supplements and traditional herbs in China. However, the quality of calyxes is uneven, and it is prone to getting moldy or moth-eaten during storage. High-performance liquid chromatography (HPLC) fingerprints and multivariate chemometric methods were combined to evaluate quality, and three representative compounds were chosen as the quality markers (Q-markers). Hierarchical cluster analysis (HCA) and principal component analysis (PCA) provided a clear discrimination of PALF samples. Through further verification by partial least squares discriminant analysis (PLS-DA), backpropagation artificial neural network (BP-ANN), machine learning, and combination with the determination of the content, biology, and pharmacology effect judgment, galuteolin, rutin, and physalin O could be used as Q-markers that their contents affect the quality of PALF grade evaluation. A simple method was established to rapidly assess the quality of PALF that is important for its clinical application and storage and provide a reference for evaluating the quality of materials used in Chinese medicine.

Effect of Ginger on Chemical Composition, Physical and Sensory Characteristics of Chicken Soup

In order to investigate the effect of ginger on taste components and sensory characteristics in chicken soup, the content of amino acids, organic acids, 5′-nucleotides, and mineral elements were determined in chicken soup sample. With the ginger added, free amino acids in chicken soup obviously increased and exceeded the total amounts in ginger soup and chicken soup. The content of glutamic acid (122.74 μg/mL) was the highest among 17 free amino acids in ginger chicken soup. Meanwhile, six organic acids detected in chicken soup all obviously increased, among which lactic acid (1523.58 μg/mL) and critic acid (4692.41 μg/mL) exceeded 1000 μg/mL. The content of 5′-nucleotides had no obvious difference between ginger chicken soup and chicken soup. Compared with chicken soup, ginger chicken soup had a smaller particle size (136.43 nm) and color difference (79.69), but a higher viscosity. With ginger added in chicken soup, the content of seven mineral elements was reduced, and the content of total sugar increased. Results from an electronic tongue indicated a difference in taste profiles among the soups. The taste components and sensory quality of chicken soup were obviously affected by adding the ginger.

A smart spectral analysis strategy-based UV and FT-IR spectroscopy fingerprint: Application to quality evaluation of compound liquorice tablets

This study focuses on development of a smart spectral analysis strategy for rapid quality evaluation of complex sample. Firstly, the ultraviolet (UV) and Fourier Transform Infrared (FT-IR) spectroscopy were established. Secondly, the second derivative UV spectral was obtained and showed 7 major absorption peaks, which was the projection of the 3D-spectrum profile. It can perform peak matching like chromatogram, thus, helpful for 3D UV spectrum analysis, qualitatively and quantitatively. The qualitative and quantitative similarity results based on systematic quantified fingerprint method displayed basically a consistency with their hierarchical cluster analysis results. Notably, the quality evaluation of the first proposed FT-IR spectral quantized fingerprints and the good correlation of P_m% with PA (R² = 0.80296), as well as the excellent quantitative prediction model for liquiritin, glycyrrhizinic acid and sodium benzoate all indicated the promising of FT-IR spectral quantized fingerprint in quantitative analysis and QC of compound liquorice tablets. Finally, an integrated evaluate strategy was developed by mean algorithm to reduce the error caused by single technique. 54 samples integrally had a good quality consistency as their quality ranged grade 1-5. This study illustrated that the smart data analysis strategy based on spectral fingerprint has potential to enhance existing methodologies for further rapid and integrated studies evaluating the quality of herbal medicine and its related products.

Terahertz spectroscopy combined with data dimensionality reduction algorithms for quantitative analysis of protein content in soybeans

Protein content in soybean is a key determinant of its nutritional and economic value. The paper investigated the feasibility of terahertz (THz) spectroscopy and dimensionality reduction algorithms for the determination of protein content in soybean. First of all, the THz sample spectrum was data processed by pre-processing or dimensionality reduction algorithms. Secondly, by calibration set, using partial least squares regression (PLSR), genetic algorithms-support vector regression (GA-SVR), grey wolf optimizer-support vector regression (GWO-SVR) and back propagation neural network (BPNN) were respectively used to model protein content determination. Afterwards, the model was validated by the prediction set. Ultimately, the BPNN model combined with linear discriminant analysis (LDA) for related coefficient of prediction set (Rp), root mean square error of prediction set (RMSEP), relative standard deviation (RSD), the time required for the operation was respectively 0.9677, 1.2467%, 3.3664%, and 53.51 s. The experimental results showed that the rapid and accurate quantitative determination of protein in soybean using THz spectroscopy is feasible after a suitable dimensionality reduction algorithm.

Rapid qualitative identification and quantitative analysis of Flos Mume based on Fourier transform near infrared spectroscopy

Flos Mume, an ancient Chinese plant, is widely used for food and medicine. There are numerous varieties of Flos Mume, whose main active components are chlorogenic acid, hyperoside and isoquercitrin. Currently, Flos Mume varieties are mainly distinguished by physical appearance and they have not been scientifically indexed for identification. Fourier transform near infrared spectroscopy (FT-NIR) is a technique that when combined with chemometrics, determines internal components of samples and classifies them. Here, to distinguish between different Flos Mume varieties, we used a qualitative identification model based on FT-NIR. Various model parameters indicated its stability and high predictive performance. We developed a rapid, non-destructive method of simultaneously analyzing 8 components but found that only neochlorogenic acid, chlorogenic acid, rutin, hyperoside, and isoquercitrin have application value. Other components were excluded due to low concentration and poor prediction. Chemometric analysis found that chlorogenic acid become an ingredient which is quite different in the different categories. The content of chlorogenic acid were the highest among these components. Different varieties of Flos Mume were distinguished based on chlorogenic acid content, indicating that chlorogenic acid has potential to become a key indicator for application in quality evaluation. The established FT-NIR model for chlorogenic acid detection had excellent predictive capacity. FT-NIR was the first time applied to Flos Mume and our findings offer theoretical reference for the rapid identification and quantitative analysis of Flos Mume based on FT-NIR. Flos Mume could be evaluated for quality quickly and easily by means of FT-NIR spectroscopy.