Mass spectrometry-based electronic nose to authenticate 100% Italian durum wheat pasta and characterization of volatile compounds

Change in volatile profiles of wheat flour during maturation

The volatile profiles of wheat flour during maturation were examined through headspace solid-phase micro-extraction gas chromatography-mass spectrometry (HS-SPME-GC/MS) combined with electronic nose (E-nose) and electronic tongue (E-tongue) analyses. The wheat flour underwent maturation under three distinct conditions for predetermined durations. While GC/MS coupled with E-tongue exhibited discernment capability among wheat flour samples subjected to varying maturation conditions, E-nose analysis solely relying on principal component analysis failed to achieve discrimination. 83 volatile compounds were identified in wheat flour, with the highest abundance observed in samples matured for 50 d at 25 °C. Notably, trans-2-Nonenal, decanal, and nonanal were the main contributors to the characteristic flavor profile of wheat flour. Integration of HS-SPME-GC/MS with E-tongue indicated superior flavor development and practical viability in wheat flour matured for 50 d at 25 °C. This study furnishes a theoretical groundwork for enhancing the flavor profiles of wheat flour and its derivative products.

Impact of semolina-barley mixture on the volatolomic profile of dough and pasta: characterization by a multivariate chemometric approach

BACKGROUND

Barley flour, known to be rich in various phytochemicals, has been demonstrated to improve the technological and nutritional properties of pasta; however, its volatile profile, on which its aromatic properties depend, also plays an important role in the acceptance of barley-enriched pasta. In the present work, volatile organic compounds (VOCs) of semolina doughs enriched with different percentages of barley and of the related pasta were characterized by solid phase micro-extraction (HS-SPME) coupled to gas-chromatography/mass spectrometry (GC-MS), and evaluated using a multivariate statistical approach, including principal component analysis (PCA), cluster heatmaps, Pearson's and Spearman's correlations, and partial least squares correlation (PLSC).

RESULTS

The effects of single raw materials, and their interactions, were studied to establish their importance in the volatile profile of the samples, and the correlation between the dough VOCs and the processed product VOCs was assessed. The presence of barley flour markedly affected the volatile profile in comparison with the dough obtained with only durum wheat. For alcohols, esters, terpenes, and some aldehydes there was a clear correlation with the percentage of barley. For some of the VOCs, on the other hand, a strong dependence on the ingredients interaction effect due to the mixing stage has been demonstrated.

CONCLUSION

The heatmaps allowed a good graphical visualization of the relationship between molecules and barley percentage, offering the possibility to select the best one according to the desired volatolomic footprint. Pasta with 40% of barley was demonstrated to give pasta with the most complex volatile profile. © 2024 Society of Chemical Industry.

Volatilomics as a tool to ascertain food adulteration, authenticity, and origin

Over recent years, there has been an increase in the number of reported cases of food fraud incidents, whereas at the same time, consumers demand authentic products of high quality. The emerging volatilomics technology could be the key to the analysis and characterization of the quality of different foodstuffs. This field of omics has aroused the interest of scientists due to its noninvasive, rapid, and cost-profitable nature. This review aims to monitor the available scientific information on the use of volatilomics technology, correlate it to the relevant food categories, and demonstrate its importance in the food adulteration, authenticity, and origin areas. A comprehensive literature search was performed using various scientific search engines and "volatilomics," "volatiles," "food authenticity," "adulteration," "origin," "fingerprint," "chemometrics," and variations thereof as keywords, without chronological restriction. One hundred thirty-seven relevant publications were retrieved, covering 11 different food categories (meat and meat products, fruits and fruit products, honey, coffee, tea, herbal products, olive oil, dairy products, spices, cereals, and others), the majority of which focused on the food geographical origin. The findings show that volatilomics typically involves various methods responsible for the extraction and consequential identification of volatile compounds, whereas, with the aid of data analysis, it can handle large amounts of data, enabling the origin classification of samples or even the detection of adulteration practices. Nonetheless, a greater number of specific research studies are needed to unlock the full potential of volatilomics.

Distribution of Elements in Durum Wheat Seed and Milling Products: Discrimination between Cultivation Methods through Multivariate Data Analysis

Many staple foods originate from durum wheat and its milling products; because of this, it is very important to know their characteristics. This study investigates elemental contents in these products and if differences exist because of organic farming. The concentrations of 28 elements in the whole seed and in milling products, that is, bran, semolina and flour, of durum wheat, were determined through ICP-OES. The wheats were grown under conventional or organic agronomic practices to verify the possibility of discriminating, using the elemental content, between products coming from one or the other practice. The elements were more abundant in the outer layer of the seed, the bran, but most of them were also present in the others. Traces of Sb were present only in 3% of the samples, while traces of Tl were detected in approximately half of the seed and bran samples but not in other samples. The absence of an element was more characteristic of specific products, e.g., most semolina and flour lacked Co, while other elements showed small differences between products from organic and conventional cultivation or between different milling products, which was the case, for example, for traces of Ag, B, and V. The concentrations of these elements were coupled with multivariate discriminant analysis, specifically PLS-DA, to identify the cultivation provenance of the milled products. A few elements, although different for each product, are sufficient to attain precision and accuracy of classification close to 1; small differences exist for different products. The worst is flour, where the predicted precision and accuracy are 0.92, although using only three elements: B, K, and Se. Semolina attains perfect prediction when also adding to the three previous elements, Ag, Cd, and Cu. Further elements are necessary for bran, while Fe and Mg replace K and Ag to classify seeds. In conclusion, five elements, B, Cd, Cu, K, and Se, are the most important in distinguishing between organic and conventional agriculture; these elements also permit some differentiation among products. The method could help in fraud prevention.

Geographical traceability of soybean: An electronic nose coupled with an effective deep learning method

The quality of soybeans is correlated with their geographical origin. It is a common phenomenon to replace low-quality soybeans from substandard origins with superior ones. This paper proposes the adaptive convolutional kernel channel attention network (AKCA-Net) combined with an electronic nose (e-nose) to achieve soybean quality traceability. First, the e-nose system is used to collect soybean gas information from different origins. Second, depending on the characteristics of the gas information, we propose the adaptive convolutional kernel channel attention (AKCA) module, which focuses on key gas channel features adaptively. Finally, the AKCA-Net is proposed, which is capable of modeling deep gas channel interdependency efficiently, realizing high-precision recognition of soybean quality. In comparative experiments with other attention mechanisms, AKCA-Net demonstrated superior performance, achieving an accuracy of 98.21%, precision of 98.57%, and recall of 98.60%. In conclusion, the combination of the AKCA-Net and e-nose provides an effective strategy for soybean quality traceability.

Effects of the fermentation quality and microbial community of waxy maize mixed with fodder soybean silage

Waxy maize (Zea mays L. sinensis Kulesh) is highly regarded for its high nutritional content and unique taste. Although the stalks and leaves contain high carbohydrate levels after ear harvesting, inadequate crude protein (CP) limits the utilization and promotion of waxy maize silage in animal husbandry. In this study, waxy maize and fodder soybeans were mixed for sowing in different proportions [1:0 (CK), 1:1 (A1), 1:2 (A2), 1:3 (A3), and 1:4 (A4)] to investigate the effects of different mixing ratios on the growth of the waxy maize, the chemical indices, fermentation quality, and the microbial community of the mixed silage after ear harvesting. The mixed planting of waxy maize and fodder soybeans in different proportions had no effect on the yield and quality of the waxy maize ears and increased the aboveground biomass after ear harvesting. After ear harvesting, the neutral detergent fiber (NDF) and acid detergent fiber (ADF) contents significantly decreased, and the CP content and relative feeding value (RFV) gradually increased in the mixed silage. The pH of the treatments was lower than 4.2 except for A4, and the lowest ammonia nitrogen (AN) concentration was observed in A3. With increasing proportions of fodder soybeans, the abundance of beneficial bacteria increased and that of harmful bacteria decreased; Firmicutes and Lactobacillus were the dominant phylum and genus, respectively, and both increased gradually. Redundancy analysis (RDA) revealed that the fermentation indices affecting the microbial community composition in the silage were inconsistent among the different mixed sowing combinations. The Mantel test showed that the composition of the microbial communities in the treatments was significantly correlated with the ADF, water-soluble carbohydrate (WSC), and propionic acid (PA) contents. Comprehensive analysis revealed that the optimal mixed sowing ratio of waxy maize to fodder soybeans was 1:3, and waxy maize and fodder soybeans silage can increase the utilization of aboveground biomass and improve the fermentation quality and feeding quality of silage by changing the microbial community. These findings lay a certain theoretical foundation for improving the utilization of waxy maize.

Insights into "wheat aroma": Analysis of volatile components in wheat grains cultivated in saline-alkali soil

The wheat grains that are cultivated in saline-alkali soil exhibit a richer "wheat aroma" compared to their counterparts. This study characterized the composition and content of volatiles in five wheat kernel varieties, harvested from two fields with varying pH levels and total salt content in the soil. The wheat grown in soil with high pH and total salt content had significantly lower levels (p < 0.05) of ethyl 3-methylbutanoate and 1-octen-3-one and significantly higher levels (p < 0.05) of 1-butanol and 1-octen-3-ol. Among all factors, plant site contributed the highest F-value contribution rate (more than 77 %) for these four volatile compounds. Six e-nose sensors responsive to these four compounds exhibited consistent trends. Therefore, the lower of ethyl 3-methylbutanoate and 1-octen-3-one, the higher of 1-butanol and 1-octen-3-ol in wheat, grown on saline-alkali soil, served as characteristic markers for "wheat aroma".

Breath volatile organic compounds (VOCs) as biomarkers for the diagnosis of pathological conditions: A review

Normal and abnormal/pathological status of physiological processes in the human organism can be characterized through Volatile Organic Compounds (VOCs) emitted in breath. Recently, a wide range of volatile analytes has risen as biomarkers. These compounds have been addressed in the scientific and medical communities as an extremely valuable metabolic window. Once collected and analysed, VOCs can represent a tool for a rapid, accurate, non-invasive, and painless diagnosis of several diseases and health conditions. These biomarkers are released by exhaled breath, urine, faeces, skin, and several other ways, at trace concentration levels, usually in the ppbv (μg/L) range. For this reason, the analytical techniques applied for detecting and clinically exploiting the VOCs are extremely important. The present work reviews the most promising results in the field of breath biomarkers and the most common methods of detection of VOCs. A total of 16 pathologies and the respective database of compounds are addressed. An updated version of the VOCs biomarkers database can be consulted at: https://neomeditec.com/VOCdatabase/.

Pattern-recognizing-assisted detection of mildewed wheat by Dyes/Dyes-Cu-MOF paper-based colorimetric sensor array

In order to timely discriminate wheat with different mildew rates, a Dyes/Dyes-Cu-MOF paper-based colorimetric sensor array was designed. Using array points to capture volatile gases of wheat with different mildew rates, and output RGB values. The correlation between ΔR/ΔG/ΔB values and odor components was established. The ΔG values of array points 2' and 3' showed the best correlation with mildew rate, with R² of 0.9816 and 0.9642. The ΔR value of 3 and the ΔG value of 2 correlate well with the mildew rate, with R² of 0.9625 and 0.9502, respectively. Then, the ΔRGB values are subjected to pattern recognition processing, and LDA achieves 100% correct discrimination for all samples, or divides high and low mildew areas. This method provides an odor-based monitoring tool for fast, visual and nondestructive evaluation of food safety and quality through visualization of odors produced by different mildew rates.

Rapid analysis of meat floss origin using a supervised machine learning-based electronic nose towards food authentication

Authentication of meat floss origin has been highly critical for its consumers due to existing potential risks of having allergic diseases or religion perspective related to pork-containing foods. Herein, we developed and assessed a compact portable electronic nose (e-nose) comprising gas sensor array and supervised machine learning with a window time slicing method to sniff and to classify different meat floss products. We evaluated four different supervised learning methods for data classification (i.e., linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbors (k-NN), and random forest (RF)). Among them, an LDA model equipped with five-window-extracted feature yielded the highest accuracy values of >99% for both validation and testing data in discriminating beef, chicken, and pork flosses. The obtained e-nose results were correlated and confirmed with the spectral data from Fourier-transform infrared (FTIR) spectroscopy and gas chromatography-mass spectrometry (GC-MS) measurements. We found that beef and chicken had similar compound groups (i.e., hydrocarbons and alcohol). Meanwhile, aldehyde compounds (e.g., dodecanal and 9-octadecanal) were found to be dominant in pork products. Based on its performance evaluation, the developed e-nose system shows promising results in food authenticity testing, which paves the way for ubiquitously detecting deception and food fraud attempts.