Direct authentication and composition quantitation of red wines based on Tri-step infrared spectroscopy and multivariate data fusion

Fusing 1H NMR and Raman experimental data for the improvement of wine recognition models

The present study proposes the development of new wine recognition models based on Artificial Intelligence (AI) applied to the mid-level data fusion of 1H NMR and Raman data. In this regard, a supervised machine learning method, namely Support Vector Machines (SVMs), was applied for classifying wine samples with respect to the cultivar, vintage, and geographical origin. Because the association between the two data sources generated an input space with a high dimensionality, a feature selection algorithm was employed to identify the most relevant discriminant markers for each wine classification criterion, before SVM modeling. The proposed data processing strategy allowed the classification of the wine sample set with accuracies up to 100% in both cross-validation and on an independent test set and highlighted the efficiency of 1H NMR and Raman data fusion as opposed to the use of a single-source data for differentiating wine concerning the cultivar and vintage.

The method based on ATR-FTIR spectroscopy combined with feature variable selection for the boletus species and origins identification

Wild boletus mushrooms, which are macrofungi of the phylum Basidiomycetes, are a nutritious and unique natural food that is widely enjoyed. Since boletus are consumed with problems of indistinguishable toxic and non-toxic species and heavy metal enrichment, their species identification and traceability are crucial in ensuring quality and safety of consumption. In this study, the attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy technique combined with three feature variable extraction methods, manual selection method, semi-manual selection method, and algorithm method, were used to improve the accuracy and computational speed of the model identification, and the models were established for the identification of boletus species with an accuracy of up to 100% as well as for the identification of boletus origin with an accuracy of 86.36%. It was found that the best methods to improve the accuracy of the models were semi-manual selection, manual selection and algorithmic selection in that order. This study can provide rapid and accurate species identification and origin traceability of wild boletus, and provide theoretical basis for the rational use of feature variable selection methods.

Rapid and accurate quantification of trypsin activity using integrated infrared and ultraviolet spectroscopy with data fusion techniques

Proteases play a crucial role in industrial enzyme formulations, with activity fluctuations significantly impacting product quality and yield. Therefore, developing a method for precise and rapid detection of protease activity is paramount. This study aimed to develop a rapid and accurate method for quantifying trypsin activity using integrated infrared (IR) and ultraviolet (UV) spectroscopy combined with data fusion techniques. The developed method evaluates the enzymatic activity of trypsin under varying conditions, including temperature, pH, and ionic strength. By comparing different data fusion methods, the study identifies the optimal model for accurate enzyme activity prediction. The results demonstrated significant improvements in predictive performance using the feature-level data fusion approach. Additionally, substituting the spectral data of the samples in the validation sets into the best prediction model resulted in a minimal residual difference between predicted and true values, further verifying the model's accuracy and reliability. This innovative approach offers a practical solution for the efficient and precise quantification of enzyme activity, with broad applications in industrial processes.

A Novel Data Fusion Strategy of GC-MS and 1H NMR Spectra for the Identification of Different Vintages of Maotai-flavor Baijiu

Counterfeit Baijiu has been emerging because of the price variances of real-aged Chinese Baijiu. Accurate identification of different vintages is of great interest. In this study, the combination of gas chromatography-mass spectrometry (GC-MS) and proton nuclear magnetic resonance (1H NMR) spectroscopy was applied for the comprehensive analysis of chemical constituents for Maotai-flavor Baijiu. Furthermore, a novel data fusion strategy combined with machine learning algorithms has been established. The results showed that the midlevel data fusion combined with the random forest algorithm were the best and successfully applied for classification of different Baijiu vintages. A total of 14 differential compounds (belonging to fatty acid ethyl esters, alcohols, organic acids, and aldehydes) were identified, and used for evaluation of commercial Maotai-flavor Baijiu. Our results indicated that both volatiles and nonvolatiles contributed to the vintage differences. This study demonstrated that GC-MS and 1H NMR spectra combined with a data fusion strategy are practical for the classification of different vintages of Maotai-flavor Baijiu.

Novel Developments and Progress in Two-Dimensional Correlation Spectroscopy (2D-COS)

This first of the two-part series of the comprehensive survey review on the progress of the two-dimensional correlation spectroscopy (2D-COS) field during the period 2021-2022, covers books, reviews, tutorials, novel concepts and theories, and patent applications that appeared in the last two years, as well as some inappropriate use or citations of 2D-COS. The overall trend clearly shows that 2D-COS is continually growing and evolving with notable new developments. The technique is well recognized as a powerful analytical tool that provides deep insights into systems in many science fields.

Diverse Applications of Two-Dimensional Correlation Spectroscopy (2D-COS)

This second of the two-part series of a comprehensive survey review provides the diverse applications of two-dimensional correlation spectroscopy (2D-COS) covering different probes, perturbations, and systems in the last two years. Infrared spectroscopy has maintained its top popularity in 2D-COS over the past two years. Fluorescence spectroscopy is the second most frequently used analytical method, which has been heavily applied to the analysis of heavy metal binding, environmental, and solution systems. Various other analytical methods including laser-induced breakdown spectroscopy, dynamic mechanical analysis, differential scanning calorimetry, capillary electrophoresis, seismologic, and so on, have also been reported. In the last two years, concentration, composition, and pH are the main effects of perturbation used in the 2D-COS fields, as well as temperature. Environmental science is especially heavily studied using 2D-COS. This comprehensive survey review shows that 2D-COS undergoes continuous evolution and growth, marked by novel developments and successful applications across diverse scientific fields.

Characterization of lamb shashliks with different roasting methods by intelligent sensory technologies and GC-MS to simulate human muti-sensation: Based on multimodal deep learning

To simulate the functions of olfaction, gustation, vision, and oral touch, intelligent sensory technologies have been developed. Headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC/MS) with electronic noses (E-noses), electronic tongues (E-tongues), computer vision (CVs), and texture analyzers (TAs) was applied for sensory characterization of lamb shashliks (LSs) with various roasting methods. A total of 56 VOCs in lamb shashliks with five roasting methods were identified by HS-SPME/GC-MS, and 21 VOCs were identified as key compounds based on OAV (>1). Cross-channel sensory Transformer (CCST) was also proposed and used to predict 19 sensory attributes and their lamb shashlik scores with different roasting methods. The model achieved satisfactory results in the prediction set (R2 = 0.964). This study shows that a multimodal deep learning model can be used to simulate assessor, and it is feasible to guide and correct sensory evaluation.

Application of Fourier-Transform Infrared Spectroscopy for the Assessment of Wine Spoilage Indicators: A Feasibility Study

Wine aroma is one of the most frequently used and explored quality indicators. Typically, its assessment involves estimating the volatile composition of wine or highly trained assessors conducting sensory analysis. However, current methodologies rely on slow, expensive and complicated analytical procedures. Additionally, sensory evaluation is inherently subjective in nature. Therefore, the aim of this work is to verify the feasibility of using FTIR spectroscopy as a fast and easy methodology for the early detection of some of the most common off-odors in wines. FTIR spectroscopy was combined with partial least squares (PLS) regression for the simultaneous measurement of isoamyl alcohol, isobutanol, 1-hexanol, butyric acid, isobutyric acid, decanoic acid, ethyl acetate, furfural and acetoin. The precision and accuracy of developed calibration models (R2P > 0.90, range error ratio > 12.1 and RPD > 3.1) proved the ability of the proposed methodology to quantify the aforementioned compounds.

Recent Advances and Future Perspectives in the E-Nose Technologies Addressed to the Wine Industry

Electronic nose devices stand out as pioneering innovations in contemporary technological research, addressing the arduous challenge of replicating the complex sense of smell found in humans. Currently, sensor instruments find application in a variety of fields, including environmental, (bio)medical, food, pharmaceutical, and materials production. Particularly the latter, has seen a significant increase in the adoption of technological tools to assess food quality, gradually supplanting human panelists and thus reshaping the entire quality control paradigm in the sector. This process is happening even more rapidly in the world of wine, where olfactory sensory analysis has always played a central role in attributing certain qualities to a wine. In this review, conducted using sources such as PubMed, Science Direct, and Web of Science, we examined papers published between January 2015 and January 2024. The aim was to explore prevailing trends in the use of human panels and sensory tools (such as the E-nose) in the wine industry. The focus was on the evaluation of wine quality attributes by paying specific attention to geographical origin, sensory defects, and monitoring of production trends. Analyzed results show that the application of E-nose-type sensors performs satisfactorily in that trajectory. Nevertheless, the integration of this type of analysis with more classical methods, such as the trained sensory panel test and with the application of destructive instrument volatile compound (VOC) detection (e.g., gas chromatography), still seems necessary to better explore and investigate the aromatic characteristics of wines.

Rapid quantification of the adulteration of pomegranate juices by Raman spectroscopy and chemometrics

The juice drink industry has repeatedly been exposed to adulteration. Unscrupulous producers, for example, use cheap juice for substitution in the pursuit of more significant economic benefits, which presents a tremendous challenge for the control of the quality of drinks. The objective of this study was to apply Raman spectroscopy combined with chemometrics to rapidly quantify the adulteration concentration of apple juice or grape juice in pomegranate juice. Two supervised learning algorithms: partial least squares regression (PLSR) and support vector machine regression (SVR) were used to analyze the Raman spectra of 114 samples. The coefficient of determination (R2), root mean square error (RMSE), and residual prediction deviation (RPD) of the prediction set when using PLSR and SVR to predict the adulterated concentration of apple juice in pomegranate juice were 0.9357 and 0.9465, 6.446% and 5.974%, 3.945 and 4.322, respectively. The R2, RMSE, and RPD of the prediction set when using PLSR and SVR to predict the adulteration concentration of grape juice in pomegranate juice were 0.9501 and 0.9502, 6.334% and 5.571%, and 4.475 and 4.481, respectively. It was concluded that Raman spectroscopy combined with chemometrics has excellent potential for application as a rapid quantitative method to detect adulterated concentrations of pomegranate juice.

Direct Identification and Quantitation of Protein Peptide Powders Based on Multi-Molecular Infrared Spectroscopy and Multivariate Data Fusion

Given that protein peptide powders (PPPs) from different biological sources were inherited with diverse healthcare functions, which aroused adulteration of PPPs. A high-throughput and rapid methodology, united multi-molecular infrared (MM-IR) spectroscopy with data fusion, could determine the types and component content of PPPs from seven sources as examples. The chemical fingerprints of PPPs were thoroughly interpreted by tri-step infrared (IR) spectroscopy, and the defined spectral fingerprint region of protein peptide, total sugar, and fat was 3600-950 cm^-1, which constituted MIR finger-print region. Moreover, the mid-level data fusion model was of great applicability in qualitative analysis, in which the F1-score reached 1 and the total accuracy was 100%, and a robust quantitative model was established with excellent predictive capacity (R_p: 0.9935, RMSEP: 1.288, and RPD: 7.97). MM-IR coordinated data fusion strategies to achieve high-throughput, multi-dimensional analysis of PPPs with better accuracy and robustness which meant a significant potential for the comprehensive analysis of other powders in food as well.

A comparative study on the taste quality of Mytilus coruscus under different shucking treatments

Shucking is an indispensable step in the preparation of cooked mussel products, as it facilitates the detachment of meat from the shell. In this study, we comprehensively investigated the effects of boiling, steaming, and microwaving on taste constituents in half-cooked mussel meat. Two-dimensional correlation spectroscopy revealed the key differential taste components of the different shucking groups. Structural equation modeling (SEM) indicated the positive effects of saltiness and bitterness on umami taste, while sweetness and sourness had negative effects on umami taste in half-cooked mussel meat. Furthermore, Glu, Asp, Ala, Arg, betaine, malic acid, succinic acid, glycogen, Cl^-, Na⁺, K⁺, and PO3- 4 were quantitatively determined as the main taste compounds. The steaming shelling group had the most enriched taste components, with the highest equivalent umami concentration compared to the other shelling groups. Hence, steaming shucking may be favored due to abundant tastes and nutrients.

Effects of temperature fluctuations on non-volatile taste compounds in tilapia fillets (Oreochromis niloticus)

In this study, the effect of temperature fluctuations on the taste quality of tilapia fillets during frozen storage was investigated. Major temperature-responsive factors included free amino acids (FAAs) and flavor nucleotides in fish fillets, which were identified using multidimensional infrared spectroscopy (MM-IR). The main FAA in tilapia fillets is a sweet amino acid (glycine). Compared with the control group, the umami FAAs and sweetness FAAs were significantly increased, and the total FAAs content increased to 1.30 times after the ninth fluctuation, reaching the highest level (611.16 ± 73.60 mg/100 g). Considering the equivalent umami intensity values, adenosine monophosphate and inosine monophosphate were retained during the first and second temperature fluctuations. In addition, the content of Na⁺, K⁺, and Ca²⁺ decreased (P < 0.05). Therefore, MM-IR is an effective method to identify taste components. With regard to taste quality, temperature fluctuations in the twofold range have an umami-enhancing effect.

Effect of different drying methods on free amino acid and flavor nucleotides of scallop (patinopecten yessoensis) adductor muscle

The effects of hot air drying (HAD), vacuum hot air drying (VHAD), microwave drying (MWD), and vacuum freeze drying (VFD) on free amino acids (FAAs) and flavor nucleotides in scallop adductor muscle (SAM) were studied. The liquid chromatography and multidimensional infrared spectroscopy (MM-IR) were used. Compared with fresh SAM, the main FAAs were glycine, alanine, arginine, and glutamic acid in dried SAM. The total FAAs content in VFD group was 1.40-1.90 times of the other group. The umami taste nucleotides (IMP and AMP) content in the VFD and MWD groups was significantly higher than that in HAD and VHAD groups. Equivalent umami concentrations were found: VFD > MWD > VHAD > HAD. MM-IR analysis was an efficient method for identifying taste components. The results revealed FAAs and flavor nucleotides and the mutual adjustment of compounds were related to drying method, and VFD was preferred for taste substance retention in scallops.

Early Discrimination and Prediction of C. fimbriata-Infected Sweetpotatoes during the Asymptomatic Period Using Electronic Nose

Sweetpotato is prone to disease caused by C. fimbriata without obvious lesions on the surface in the early period of infection. Therefore, it is necessary to explore the possibility of developing an efficient early disease detection method for sweetpotatoes that can be used before symptoms are observed. In this study, sweetpotatoes were inoculated with C. fimbriata and stored for different lengths of time. The total colony count was detected every 8 h; HS-SPME/GC–MS and E-nose were used simultaneously to detect volatile compounds. The results indicated that the growth of C. fimbriata entered the exponential phase at 48 h, resulting in significant differences in concentrations of volatile compounds in infected sweetpotatoes at different times, especially toxic ipomeamarone in ketones. The contents of volatile compounds were related to the responses of the sensors. E-nose was combined with multiple chemometrics methods to discriminate and predict infected sweetpotatoes at 0 h, 48 h, 64 h, and 72 h. Among the methods used, linear discriminant analysis (LDA) had the best discriminant effect, with sensitivity, specificity, precision, and accuracy scores of 100%. E-nose combined with K-nearest neighbours (KNN) achieved the best predictions for ipomeamarone contents and total colony counts. This study illustrates that E-nose is a feasible and promising technology for the early detection of C. fimbriata infection in sweetpotatoes during the asymptomatic period.