Aromatic Fingerprints: VOC Analysis with E-Nose and GC-MS for Rapid Detection of Adulteration in Sesame Oil

Overview of industrial food fraud and authentication through chromatography technique and its impact on public health

Food fraud is widespread nowadays in the food products supply chain, from raw materials processing to the final product and during storage and transport. The most frequent fraud is practiced in staple food commodities like cereals. Their origin, variety, genotype, and bioactive compounds are altered to deceive consumers. Similarly, in various food sectors like beverage, baking, and confectionary, items like melamine, flour improver, and food colors are used in the market to temple consumers. To tackle food fraud and authentication, non-destructive techniques are being used. These techniques have limitations like lack of standardization, interference from multiple absorbing species, ambiguous results, and time-consuming to perform, depending on the type, size, and location of the system proved difficult to quantify the samples of adulteration. Chromatography has been introduced as an effective technique. It serves to safeguard public health due to its detection capabilities. Chromatography proved a crucial tool against fraudulent practices to preserve consumer trust.

An Exploratory Study on the Rapid Detection of Volatile Organic Compounds in Gardenia Fruit Using the Heracles NEO Ultra-Fast Gas Phase Electronic Nose

Gardenia fruit is a popular functional food and raw material for natural pigments. It comes from a wide range of sources, and different products sharing the same name are very common. Volatile organic compounds (VOCs) are important factors that affect the flavor and quality of gardenia fruit. This study used the Heracles NEO ultra-fast gas phase electronic nose with advanced odor analysis performance and high sensitivity to analyze six batches of gardenia fruit from different sources. This study analyzed the VOCs to find a way to quickly identify gardenia fruit. The results show that this method can accurately distinguish the odor characteristics of various gardenia fruit samples. The VOCs in gardenia fruit are mainly organic acid esters, ketones, and aldehyde compounds. By combining principal component analysis (PCA) and discriminant factor analysis (DFA), this study found that the hexanal content varied the most in different gardenia fruit samples. The VOCs allowed for the fruit samples to be grouped into two main categories. One fruit sample was quite different from the fruits of other origins. The results provide theoretical support for feasibility of rapid identification and quality control of gardenia fruit and related products in the future.

Complementary assessment of nano-packaged garlic properties by electronic nose

It is crucial to initiate appropriate storage conditions for garlic depending on its properties. Fungal contamination can reduce the quality of garlic through changes in its properties which result in its aroma alteration. This study aimed to evaluate the effects of treatments such as fungal infection (FI), material of packaging (MP), and storage duration (SD) on various characteristics of garlic. An electronic nose was used complementarily to trace the aroma changes as a non-destructive indicator. The Fusarium oxysporum (FS), Alternaria embellisia (AL), and Botrytis allii (BT) fungi were adopted for inoculation. The low-density polyethylene (LDPE) and silicone nano-emulsions (SNE) were used for packing samples. The data were analyzed by diverse approaches such as ANOVA, PLS, PCA, LDA, and BPNN. The results revealed that the evaluated properties changed during the storage. The implementation of treatments altered the intensity of these changes. The highest values of weight loss (21.14%), color changes (50.21), and acidity (7.48) were observed in the FS-infected samples kept in LDPE for 28 days. The accuracy of PCA, LDA, and BPNN in the multivariate analysis of aroma had an increasing-decreasing trend. The best accuracy of PCA in categorizing the FI and MP treatments achieved in the twelfth day of storage (96%). The optimal accuracy of classifications based on FI and MP treatments was obtained at d#12 (100%) and d#24 (100%), respectively. The PLS exposed that the aroma changes in garlic had a high correlation with the changes of studied properties (R 2 ≥ .7), except for the mechanical properties.

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.

A study of volatiles of young citrus fruits from four areas based on GC-MS and flash GC e-nose combined with multivariate algorithms

The present study has successfully established a scientific and precise approach for distinguishing the geographical origins of young citrus fruits (Qingpi) from four primary production regions in China, using gas chromatography-mass spectrometry (GC-MS) and flash gas chromatography electronic nose (flash GC e-nose) to analyze the volatile composition and odor characteristics. Through the application of chemometric analysis, a clear differentiation among Qingpi samples was established using GC-MS. Additionally, the application of flash GC e-nose facilitated the extraction of flavor information, which enabled the discrimination of geographical origins. Several flavor components were identified as significant factors for origin certification. Furthermore, two pattern recognition algorithms were employed to achieve high accuracy in regional identification. The results of this investigation demonstrate that the amalgamation of multivariate chemometrics and algorithms can proficiently discern the sources of those young citrus fruits. The findings of this research can provide a reference for the assessment of quality control in food and other agricultural commodities in the times ahead.

Identification of Milk Adulteration in Camel Milk Using FT-Mid-Infrared Spectroscopy and Machine Learning Models

Camel milk, esteemed for its high nutritional value, has long been a subject of interest. However, the adulteration of camel milk with cow milk poses a significant threat to food quality and safety. Fourier-transform infrared spectroscopy (FT-MIR) has emerged as a rapid method for the detection and quantification of cow milk adulteration. Nevertheless, its effectiveness in conveniently detecting adulteration in camel milk remains to be determined. Camel milk samples were collected from Alxa League, Inner Mongolia, China, and were supplemented with varying concentrations of cow milk samples. Spectra were acquired using the FOSS FT6000 spectrometer, and a diverse set of machine learning models was employed to detect cow milk adulteration in camel milk. Our results demonstrate that the Linear Discriminant Analysis (LDA) model effectively distinguishes pure camel milk from adulterated samples, maintaining a 100% detection rate even at cow milk addition levels of 10 g/100 g. The neural network quantitative model for cow milk adulteration in camel milk exhibited a detection limit of 3.27 g/100 g and a quantification limit of 10.90 g/100 g. The quantitative model demonstrated excellent precision and accuracy within the range of 10-90 g/100 g of adulteration. This study highlights the potential of FT-MIR spectroscopy in conjunction with machine learning techniques for ensuring the authenticity and quality of camel milk, thus addressing concerns related to food integrity and consumer safety.

Design of an Artificial Intelligence of Things-Based Sesame Oil Evaluator for Quality Assessment Using Gas Sensors and Deep Learning Mechanisms

Traditional oil quality measurement is mostly based on chemical indicators such as acid value, peroxide value, and p-anisidine value. This process requires specialized knowledge and involves complex steps. Hence, this study designs and proposes a Sesame Oil Quality Assessment Service Platform, which is composed of an Intelligent Sesame Oil Evaluator (ISO Evaluator) and a Cloud Service Platform. Users can quickly assess the quality of sesame oil using this platform. The ISO Evaluator employs Artificial Intelligence of Things (AIoT) sensors to detect changes in volatile gases and the color of the oil during storage. It utilizes deep learning mechanisms, including Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) to determine and evaluate the quality of the sesame oil. Evaluation results demonstrate that the linear discriminant analysis (LDA) value is 95.13. The MQ2, MQ3, MQ4, MQ7, and MQ8 sensors have a positive correlation. The CNN combined with an ANN model achieves a Mean Absolute Percentage Error (MAPE) of 8.1820% for predicting oil quality, while the LSTM model predicts future variations in oil quality indicators with a MAPE of 0.44%. Finally, the designed Sesame Oil Quality Assessment Service Platform effectively addresses issues related to digitization, quality measurement, supply quality observation, and scalability.

FT-MIR-ATR Associated with Chemometrics Methods: A Preliminary Analysis of Deterioration State of Brazil Nut Oil

Brazil nut oil is highly valued in the food, cosmetic, chemical, and pharmaceutical industries, as well as other sectors of the economy. This work aims to use the Fourier transform infrared (FTIR) technique associated with partial least squares regression (PLSR) and principal component analysis (PCA) to demonstrate that these methods can be used in a prior and rapid analysis in quality control. Natural oils were extracted and stored for chemical analysis. PCA presented two groups regarding the state of degradation, subdivided into super-degraded and partially degraded groups in 99.88% of the explained variance. The applied PLS reported an acidity index (AI) prediction model with root mean square error of calibration (RMSEC) = 1.8564, root mean square error of cross-validation (REMSECV) = 4.2641, root mean square error of prediction (RMSEP) = 2.1491, R2cal (calibration correlation coefficient) equal to 0.9679, R2val (validation correlation coefficient) equal to 0.8474, and R2pred (prediction correlation coefficient) equal to 0, 8468. The peroxide index (PI) prediction model showed RMSEC = 0.0005, REMSECV = 0.0016, RMSEP = 0.00079, calibration R2 equal to 0.9670, cross-validation R2 equal to 0.7149, and R2 of prediction equal to 0.9099. The physical-chemical analyses identified that five samples fit in the food sector and the others fit in other sectors of the economy. In this way, the preliminary monitoring of the state of degradation was reported, and the prediction models of the peroxide and acidity indexes in Brazil nut oil for quality control were determined.