Food Adulteration Research Articles

Proto-DS: A Self-Supervised Learning-Based Nondestructive Testing Approach for Food Adulteration with Imbalanced Hyperspectral Data

Conventional food fraud detection using hyperspectral imaging (HSI) relies on the discriminative power of machine learning. However, these approaches often assume a balanced class distribution in an ideal laboratory environment, which is impractical in real-world scenarios with diverse label distributions. This results in suboptimal performance when less frequent classes are overshadowed by the majority class during training. Thus, the critical research challenge emerges of how to develop an effective classifier on a small-scale imbalanced dataset without significant bias from the dominant class. In this paper, we propose a novel nondestructive detection approach, which we call the Dice Loss Improved Self-Supervised Learning-Based Prototypical Network (Proto-DS), designed to address this imbalanced learning challenge. The proposed amalgamation mitigates the label bias on the most frequent class, further improving robustness. We validate our proposed method on three collected hyperspectral food image datasets with varying degrees of data imbalance: Citri Reticulatae Pericarpium (Chenpi), Chinese herbs, and coffee beans. Comparisons with state-of-the-art imbalanced learning techniques, including the Synthetic Minority Oversampling Technique (SMOTE) and class-importance reweighting, reveal our method’s superiority. Notably, our experiments demonstrate that Proto-DS consistently outperforms conventional approaches, achieving the best average balanced accuracy of 88.18% across various training sample sizes, whereas the Logistic Model Tree (LMT), Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN) approaches attain only 59.42%, 60.38%, and 66.34%, respectively. Overall, self-supervised learning is key to improving imbalanced learning performance and outperforms related approaches, while both prototypical networks and the Dice loss can further enhance classification performance. Intriguingly, self-supervised learning can provide complementary information to existing imbalanced learning approaches. Combining these approaches may serve as a potential solution for building effective models with limited training data.

Assess the knowledge regarding food adulteration among house wife: A study on berhampur city of Ganjam district, Odisha

Adulteration of food has occurred if it contains any substance injurious to health; it contains any filthy, putrid or decomposed substance; it is prepared, handled or stored under unsanitary conditions; diseased animals have been used in preparation; the container is made of a poisonous substance which will render the contents harmful; contains colours and preservatives other than those permitted by law; it contains pesticide residues or additives not recognised as safe etc. foods may be adulterated either intentional or incidental at all stages from production to selling. Food is adulterated if its quality is lowered or affected by the addition of substances which are injurious to health or by the removal of substances which are nutritious. The present study focuses on the awareness of the housewives among the consumers on food adulterants and to find out the extent of adulteration of the food stuffs. This study was conducted to assess the knowledge about food adulteration among 100 housewives in selected areas, Berhampur. A survey was conducted to collect the information through interview method. They must be well acquainted with the physical food characteristics such as appearance, colour, shape, texture, touch, flavour, taste etc. The urban and rural women are using the home test method for detecting food adulteration. Some urban house wives are having knowledge about food law and the rest of the housewife’s are not aware about food law. The rural housewife’s are not having any knowledge regarding food law, food standard and consumer protection Act. It can be concluded that some women are not having any knowledge regarding different adulteration programme. So, the government and people should promote the different adulteration programme. The media should take a great role to aware women in an interesting and attractive manner. Hence, it is essential to create community awareness and take interventional measures for the health and wellbeing of the community

Predicting food adulterants in milk using Support Vector Machine (SVM)

Milk adulteration is a significant global issue, particularly in emerging nations, where inadequate monitoring and unhygienic conditions prevail. The adulteration of milk with various chemicals, such as urea, water, skimmed milk powder, sugar, and detergent, poses serious health risks, including heart problems, diarrhea, CNS disorders, irritation, and gastrointestinal disorders. Traditional detection methods are labor-intensive and require sophisticated equipment, which limits their practical application. This study aims to develop a machine learning-based approach to detect milk adulteration using attributes like Solids- Not-Fat (SNF), fat, Corrected Lactometer Reading (CLR), Total Solids (TS), temperature, and protein content. Various machine learning models were employed and evaluated for their performance, including Logistic Regression, Decision Trees, SVM, and Random Forests. The findings demonstrate that machine learning can effectively identify adulteration types, providing a foundation for the dairy industry’s practical and automated detection systems. This research comprehensively reviews common milk adulterants and highlights advanced detection methods to ensure milk quality and safety.

Design & development of adulteration detection system by fumigation method & machine learning techniques

A novel method for discovery of adulteration in edible oil is proposed based on concept of refractive index and electronic sensors. The research work focusses on two distinct methodologies like employing datasets and implementing a fumigation technique that integrates real-time hardware for testing Edible oil Impurities. In the first method, the dataset taken into consideration contains spectral data collected using Advanced ATR-MIR Spectroscopy for pure oil and various levels of adulteration with Vegetable oil. Each and every edible oil has a certain value of refractive index. When such oils are contemned in a change adding adulterants, the value of its refractive indices also changes. This value of refractive index serves as a feature for testing the oil and helps us in detecting the adulteration. If Oil is adulterated with vegetable oils, the refractive index will be lower and with animal fats, the refractive index will be higher than that of pure Oil. While in Fumigation Method a hardware module is develop in which adulterated & pure oil samples are heated at 40–50 °C for 4.66 min and the volatiles that are generated by varying gas concentrations are forcefully passed through to the MEMS Gas Sensor-MISC-2714 and Multichannel Gas sensor. The conductance of the sensors changes according to the gases sensed by the sensors contributes to features extraction. The conductance value serves as a feature for the classifier to determine whether the sample is highly, moderately, or lowly contaminated. Thus, in proposed methods we use different algorithms based on machine learning like KNN, Random Forest, CATBOOST and XGBOOST to accurately reveal the adulteration. Amongst all the applied algorithm Random Forest (RF) Classifier & XGBOOST algorithm outperform well and gives 100% accuracy. The proposed work is used for identifying food adulteration in edible food products which helps us to feed Society with high-quality food.

Navigating Food Fraud: A Survey of Nigerian Consumer Knowledge and Attitudes.

Food fraud presents a growing threat globally, impacting consumer health; food safety; and brand credibility. The key motivation for food fraud is usually an economic or financial advantage. Economically motivated food fraud (EMA) is the illegal deception, deliberate and intentional substitution or addition of a substance in a food product, which may pose a possible health risk to unsuspecting consumers. This study aims to assess the awareness and attitudes of Nigerian consumers towards food authenticity and the regulatory measures in place to combat food adulteration. The study comprised 2160 respondents who were asked about their knowledge of high-profile food fraud incidents, frequently adulterated products, and their opinions of the effects of food fraud and preventative measures. The findings of this study revealed that more than a third of respondents were unfamiliar with the term "food fraud". However, they were aware of local high-profile cases, such as "plastic rice" and counterfeit dairy products. Most participants relied on point-of-sale information for assessing food authenticity, with street vendors being viewed as potential sources of food adulteration. The study also found that more than half of the respondents were concerned about food fraud regardless of whether it involved local or imported products. A wide variety of food items were identified as prone to adulteration or fraudulent activity. These included alcoholic drinks, dairy products, honey, rice, and tomato paste, amongst others. These findings highlight the need for improved consumer awareness, regulatory effectiveness, and remediation efforts to combat food fraud in Nigeria.

A contemporary study: Molecular approach to histological analysis of meat products by reverse-transcriptase PCR

Meat and meat products constitute an important part of human diet. Yet, in order to meet consumers' increasing demand for meat products and manufacturers’ demand to lower the expenses, usage of offal tissues in meat products are prevalent as food adulteration. Industrial food processing techniques enable producers hide the tissue texture; therefore, histological analysis methodology often fails to detect the fraud. Here, we present a contemporary and unbiased, molecular approach to traditional histological analysis of meat products by real-time reverse transcriptase polymerase chain reaction (RT-PCR). Our study includes the bioinformatic approach to select relevant transcripts that are expressed only in offal tissues but not in muscle and adipose tissues. The selected transcripts were particularly paid attention to have high expression levels for a higher possibility of being detected in processed meat products. The developed qualitative RT-PCR method was also validated by specificity, sensitivity, repeatability and reproducibility parameters.

Quantitative Analysis of High-Price Rice Adulteration Based on Near-Infrared Spectroscopy Combined with Chemometrics.

Near-infrared spectroscopy (NIRS) holds significant promise in detecting food adulteration due to its non-destructive, simple, and user-friendly properties. This study employed NIRS in conjunction with chemometrics to estimate the content of low-price rice flours (Nanjing, Songjing, Jiangxi silk, Yunhui) blended with high-price rice (Wuchang and Thai fragrant). Partial least squares regression (PLSR), support vector regression (SVR), and back-propagation neural network (BPNN) models were deployed to analyze the spectral data of adulterated samples and assess the degree of contamination. Various preprocessing techniques, parameter optimization strategies, and wavelength selection methods were employed to enhance model accuracy. With correlation coefficients exceeding 87%, the BPNN models exhibited high accuracy in estimating adulteration levels in high-price rice. The SPXY-SG-BPNN, SPXY-MMN-BPNN, KS-SNV-BPNN, and SPXY-SG-BPNN models showcased exceptional performance in discerning mixed Wuchang japonica, Thai fragrant indica, and Thai fragrant Yunhui rice. As shown above, NIRS demonstrated its potential as a rapid, non-destructive method for detecting low-price rice in premium rice blends. Future studies should be performed to concentrate on enhancing the models' versatility and practical applicability.

Rapid screening of prednisolone acetate adulterants in health foods using colloidal gold immunochromatographic assay

In this study, a rapid detection method utilizing colloidal gold immunochromatography (CG-ICA) was developed for the detection of illegally added prednisone acetate in health foods. Initially, the preparation conditions of colloidal gold solution were optimized. The optimal potassium carbonate dosage, antibody diluent type, antibody dosage, probe labeling time, blocking time and BSA dosage were determined. Technical analysis was performed to ensure that the established CG-ICA exhibited satisfactory color development and inhibition rates. Under optimized conditions, the cut-off value of CG-ICA was 250 μg/kg. The assay demonstrated a sensitivity of 100 %, a false positive rate of 8 %, and a false negative rate of 0, indicating high specificity for prednisone acetate. The results obtained from testing actual samples were consistent with those obtained using LC-MS/MS, thereby verifying the reliability of the developed method. This method offers robust support for the rapid detection of illegally added prednisone acetate in health foods.

Visible diffuse reflectance smartphone spectrometer with high spectral accuracy

A smartphone-based spectrometer employing principle of diffuse reflection is reported for the surface analysis of solid samples. The instrument utilizes a thin-film grating to diffract incoming light, while a diffuse reflecting surface projects the image of this diffracted light onto the detector plane. The CMOS camera of smartphone camera directly captures the diffusely reflected photons within its limited field-of-view thus eliminating the need for collection, conditioning and converging optics. The optical setup of the instrument provides facility to calibrate the spectral response considering the nonlinear distribution of the wavelength across the diffraction direction. Additional correction in the detector response at different light intensity results a reduced spectral error with a maximum wavelength resolution of δλ=0.08 nm/pixel in the camera within the spectral range Δλ = (400 – 700) nm. As a proof of the concept, the instrument demonstrates successful detection of color pigments in food samples by absorption measurement of the samples at an average spectral error < 6 %. The distinct absorption peak associated with standard food colors are compared against the absorption profile of unknown food colors used in pastry cake. This field-functional smart analysis with internet connectivity opens opportunity of identifying food adulteration by using toxic chemical colors at the point-of-test and immediate reporting to others. The overall instrument is fabricated by utilizing low-cost and light weight plastic wood to make compact (110 mm × 105 mm × 125 mm), robust, inexpensive (∼$ 50) and suitable for field-portable (∼145 gm) hand-held operation.

Integrating different detection techniques and data analysis methods for comprehensive food authenticity verification

Traditional food testing methods, primarily confined to laboratory settings, are increasingly inadequate to detect covert food adulteration techniques. Hence, a crucial review of recent technological strides to combat food fraud is essential. This comprehensive analysis explores state-of-the-art technologies in food analysis, accentuating the pivotal role of sophisticated data processing methods and the amalgamation of diverse technologies in enhancing food authenticity testing. The paper assesses the merits and drawbacks of distinct data processing techniques and explores their potential synergies. The future of food authentication hinges on the integration of portable smart detection devices with mobile applications for real-time food analysis, including miniaturized spectrometers and portable sensors. This integration, coupled with advanced machine learning and deep learning for robust model construction, promises to achieve real-time, on-site food detection. Moreover, effective data processing, encompassing preprocessing, chemometrics, and regression analysis, remains indispensable for precise food authentication.

A novel method for rice identification: Coupling Raman spectroscopy with Fourier spectrum and analyzing with deep learning

The development of detection methods for food adulteration is of great significance for promoting innovation and development in food safety supervision technology. In this study, we proposed a novel model: Raman-Fourier-BiLSTM-CNN (RFBC), which combines Raman spectroscopy with deep learning to achieve precise identification of seven brands of japonica rice in the northeastern region of China. This model focuses on revealing the worth of the Fourier spectrum of Raman spectra. The raw Raman spectra and their Fourier spectra are cleverly coupled through a Bi-directional Long Short-Term Memory (BiLSTM) connection structure, and the resulting composite features are deeply analyzed by a Convolutional Neural Network (CNN). Compared to traditional algorithms, RFBC exhibits superior feature extraction capabilities. To further evaluate the recognition performance of RFBC, this study compared it with the RFBC model containing only the original Raman spectrum branch, as well as with machine learning models based on Principal Component Analysis (PCA), including Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), and eXtreme Gradient Boosting (XGBoost). The results show that, compared with the other five models, RFBC can accurately identify different brands of japonica rice, with significant advantages in accuracy, precision, recall, and other aspects. RFBC achieves a classification accuracy of 97.1 %, macro precision of 97.2 %, macro recall of 97.1 %, and macro F1-score of 97.2 %. The system proposed in this study can more accurately identify brands of japonica rice, providing strong technical support for combating counterfeit japonica rice products.

Development of Multimodal Sensors for Calcium-Based Food Adulterant Using Tannic Acid Derived Functional Nanomaterials

Development of Multimodal Sensors for Calcium-Based Food Adulterant Using Tannic Acid Derived Functional Nanomaterials

The use of PCR method for adulteration detection of goat dairy products manufactured by smallholders

The analysis of food adulteration is an important element of improving health safety. Both accidental and intentional adulteration of food, can cause serious health consequences for humans and can be considered as health hazard. In recent years, molecular biology techniques has been increasingly used in food analysis due to their high precision and the ability to obtain results in a short time, as well as the identification and quantitative determination of food adulterants. The samples tested in the study were goat's milk, yogurts, fresh and ripening cheeses produced by smallholders all over Poland. The analysis included DNA isolation, which was used as template for duplex PCR and detection of goat’s and cow’s mitochondrial DNA fragments. The obtained results indicate that the applied technique enabled the rapid identification of adulteration in analyzed goat’s milk products, among which in only one product out of thirty five the presence of undeclared cow’s milk was detected. This indicates good food safety standards applied on small-scale farms.

Development and Test of Low-Cost Multi-Channel Multi-Frequency Lock-In Amplifier for Health and Environment Sensing.

Optical-based sensing techniques and instruments, such as fluorometric systems, absorbance-based sensors, and photoacoustic spectrometers, are important tools for detecting food fraud, adulteration, and contamination for health and environmental purposes. All the aforementioned optical equipments generally require one or more low-frequency Lock-In Amplifiers (LIAs) to extract the signal of interest from background noise. In the cited applications, the required LIA frequency is quite low (up to 1 kHz), and this leads to a simplification of the hardware with consequent good results in portability, reduced size, weight, and low-cost characteristics. The present system, called ENEA DSP Box Due, is based on a very inexpensive microcontroller proto-board and can replace four commercial LIAs, resulting in significant savings in both cost and space. Furthermore, it incorporates a dual-channel oscilloscope and a sinusoidal function generator. This article outlines the architecture of the ENEA DSP Box Due, its electrical characterization, and its applications within a project concerning laser techniques for food and water safety.

Food Items Commonly Found in Every Households that are Affected by Food Adulteration: A Review

Food adulteration is the act of deceiving the consumers so as to make a financial profit. Adding these adulterants change the properties of the food items by altering the colour, flavour, fragrance, appearance, texture and other properties of food products which make them appealing to consumers. Even food items like milk, dairy products, food grains, cereals, pulses fruits, vegetables, etc. are not free from adulteration. Adulteration can either be added intentionally or unintentionally, though more often, adulteration is done intentionally to make a financial benefit. To protect the consumers from these harmful adulterants, the FSSAI has placed many acts, regulations and rules to monitor and stop the production and distribution of these substances. The rate of food adulteration is increasing rapidly in India, to feed its 1.44 billion population, and across the globe. Recent global events, like the COVID-19 pandemic; rising inflation; the crisis in Sri Lanka due to bankruptcy since 2022, the ongoing Sudan conflict, the Israeli war with Palestine and the Russia-Ukraine war are some of the events that has not only has increased the demand for food supplies but has also affected the production, transport and distribution of food across the world. In this article, various commonly found food products are identified and they are reviewed with the help of various published literatures to see if the food products actually do contain adulterants.

Use of chromatography, spectroscopy and chemometrics for evaluation of cold-pressed oils

The recent increase in imitation and adulteration in food requires the control of cold-pressed oils in the market. The cold-pressed oils of these seeds (coconut, Nigella sativa, flaxseed, sesame seed) contain essential fatty acids and the fatty acids and sterol compositions are the most essential features that distinguish these oils. Chromatography and spectroscopy methods were applied in this study. Fingerprints of four reference materials obtained from coconut, black cumin, flaxseed, and sesame were determined using FTIR and Raman spectroscopy, and their fatty acids and sterol compositions were determined using GC-FID. The spectroscopic and chromatographic results of 20 cold-pressed oils supplied from the market were compared with these reference oils. Results showed differences in the values of sterol and fatty acid compositions between the reference oils and the oils supplied from the market. In the Raman and FTIR spectroscopy method, spectra describing the chemical structure of oils as fingerprints were obtained, and these spectra supported the differences in GC-FID results. The study results show that the chromatographic and spectroscopic methods are references for each other and provide information about whether the oils offered for sale in the market differ from reference oils and each other.

Unravelling red deer (Cervus elaphus) meat adulteration in gourmet foods by quantitative real-time PCR

Red deer (Cervus elaphus) meat is highly regarded as an exclusive gourmet food due to its sensory quality, nutritional value and premium price, thus being susceptible to adulteration practices by its substitution with inferior meat species. This study proposes a novel real-time PCR method targeting the troponin I gene for detecting and quantifying red deer meat in processed food products. The assay showed acceptable performance parameters, achieving absolute and relative limits of quantification of 0.01 ng of DNA and 0.5% (w/w) of red deer meat in both raw and thermally processed pâté. The assay parameters, using raw and autoclaved reference mixtures, regarding PCR efficiency (89.3% and 100.0%, respectively), slope of the calibration curve (−3.6067 and −3.3224, respectively) and R2 (0.9889 and 0.9893, respectively) highlight the high performance of the calibration models for the determination of red deer meat. The method was successfully validated using blind mixtures and applied to 46 commercial meat products from Poland, Portugal and Spain. Data suggested the adulteration of 48% of the samples by the total or partial substitution of red deer species and the suitability of the method for the routine testing of game meat products in quality control laboratories.

Quantitative predictions of protein and total flavonoids content in Tartary and common buckwheat using near-infrared spectroscopy and chemometrics

A rapid method was developed for determining the total flavonoid and protein content in Tartary buckwheat by employing near-infrared spectroscopy (NIRS) and various machine learning algorithms, including partial least squares regression (PLSR), support vector regression (SVR), and backpropagation neural network (BPNN). The RAW-SPA-CV-SVR model exhibited superior predictive accuracy for both Tartary and common buckwheat, with a high coefficient of determination (R2p = 0.9811) and a root mean squared error of prediction (RMSEP = 0.1071) for flavonoids, outperforming both PLSR and BPNN models. Additionally, the MMN-SPA-PSO-SVR model demonstrated exceptional performance in predicting protein content (R2p = 0.9247, RMSEP = 0.3906), enhancing the effectiveness of the MMN preprocessing technique for preserving the original data distribution. These findings indicate that the proposed methodology could efficiently assess buckwheat adulteration analysis. It can also provide new insights for the development of a promising method for quantifying food adulteration and controlling food quality.

Mini review: Impact of branding on consumer perception of adulterated foods

Mini review: Impact of branding on consumer perception of adulterated foods

Review: Impact of food safety on global trade.

Food safety encompasses the supply and assurance of safe, high-quality food for consumers. It is a crucial aspect of food security, gaining greater global attention due to the increasing number of widespread foodborne incidents. International trade is expanding as countries increasingly rely on each other to secure a sufficient and diverse food supply. Beyond this, concerns about food safety have become more prevalent due to various factors. Therefore, this review aims to investigate the effects of food safety-associated risks on the international trade of food and related products. A total of 37 published studies retrieved using different search engines were included in this review. This review revealed that because of rapid population growth and rising food demand in developing nations, agricultural intensification is growing. It has been found that foodborne illnesses and associated discrepancies can impede the international trade of food commodities. Trade bans due to the fear of foodborne illnesses are growing. The consequences of foodborne diseases are multifaceted and include financial losses from trade restrictions, medical costs for prevention or control, resource depletion and a decline in food production. The overall effects are increased international trade tensions and livelihood vulnerability to poverty, notably for small-scale livestock producers. Potential food contaminants include microbes, pesticides, pharmaceutical residues, heavy metals and fraudulent such as improper food processing, mislabelling, poor packaging, adulteration and substitution. Hence, countries are encouraged to harmonize the rights and duties set by the World Trade Organization under sanitary and phytosanitarys to maximize their advantages in global markets. Based on this evidence, we recommend that each country develop and integrate regulations that would ensure the safety of both domestic and international food production systems. Furthermore, the global community should either revise the current functioning food regulatory and monitoring body or establish a more genuine collaborative network.