Feasibility study of detecting some milk adulterations using a LED-based Vis-SWNIR photoacoustic spectroscopy system.

The aim of this study is to evaluate a previousely developed photoacoustic spectroscopy system with light sources of visible to short-wave near infrared (Vis-SWNIR, 395-940 nm) for detection of adulterations in cow's milk including formalin, urea, hydrogen peroxide, starch, sodium hypochlorite, and detergent powder. The results of principal component analysis (PCA) showed a very good visual differentiation of different adulterations. The artificial neural networks (ANN) showed the highest classification accuracy (97.6 %) in detection of adulteration type and adulteration level (nearly 100 %). It can be generally concluded that the Vis-SWNIR photoacoustic spectroscopy system is a reliable and potent instrument for detecting various types of milk adulterations. Further studies are suggested with including cow's milk of different sources with probable variations in composition to generalize the findings of the present study. With the extension of the light sources to the range of long-wave NIR, the system can be applied as a diagnostic tool for quality evaluation of other liquid foods.

The effect of data fusion on improving the accuracy of olive oil quality measurement.

Olive oil is one of the healthiest and most nutritious edible oils, and it has a great potential to be adulterated. In this research, fraud samples of olive oil were detected with six different classification models by fusion of two methods of E-nose and ultrasound. The samples were prepared in six categories of adulteration. The E-nose system included eight various sensors. 2 MHz probes were used in through transmission ultrasound system. Principal Component Analysis method was used to reduce features and six classification models were used for classification. Feature with the greatest influence in the classification was "percentage of ultrasonic amplitude loss." It was found that the ultrasound system's data had worked more effectively than the E-nose system. Results showed that the ANN method was recognized as the most effective classifier with the highest accuracy (95.51%). The accuracy of classification in all the classification models significantly increased with data fusion.

Development of an ultrasensitive molecularly imprinted poly-(ortho-phenylenediamine) based sensor for the determination of melamine adulteration in milk and infant formula.

A sensitive molecularly imprinted poly-(ortho-phenylenediamine) electrochemical sensor was fabricated for selective melamine detection in milk and infant formula. The pencil graphite electrode (PGE) was modified by deposition of Au nanoparticles and reduced graphene oxide (RGO) on its surface. The fabrication of the electrode in various stages was monitored using cyclic voltammetry. The immobilized RGO, MIP, and gold nanoparticles on the PGE surface were morphologically characterized by field-emission scanning electron microscopy (FESEM). Under the optimized conditions, the linear range and the limit of detection (LOD) were 10-17-10-8 M and 2.64 × 10-16 M (S/N = 3), respectively. The prepared sensor exhibited a good reproducibility and repeatability response. The recovery range of melamine-spiked milk and infant formula was 92.7%-103.9% and 93.5%-105.8%, respectively. The sensor could apply successfully for melamine determination in milk and infant formula samples.

Applications of ultrasound techniques in tandem with non-destructive approaches for the quality evaluation of edible oils.

Edible oils include triglycerides that are extracted from oil seeds or fruits such as sunflowers, palms, olives, soys, rapeseeds, cocoa and many others. They are the elementary origins of unsaturated fats and vitamins especially vitamin 'E' in people's diets. Edible oils are at risk of intentional (such as inadequate storage conditions) and unintentional adulteration, so it is necessary to pay attention to their safety, health and fraud. Generally, this evaluation can be destructive or non-destructive. There are numerous methods to evaluate quality of edible oils which include sensory analysis, chemical analysis, chromatography, ultrasound, etc. The Ultrasonic approach is a non-destructive way and also fast, accurate, inexpensive, repeatable, portable and simple. Combination of ultrasound with other techniques such as electronic nose, electronic tongue, visible spectroscopic fingerprints, chemical descriptors, Raman spectroscopy, mid-infrared and machine vision, will improve quality evaluation and detection accuracy. This review summarizes the ultrasound idea and the latest knowledge of its application with other techniques on evaluation of edible oils.

Fraud detection and quality assessment of olive oil using ultrasound.

Today, food safety is recognized as one of the most important human priorities, so effective and new policies have been implemented to improve and develop the position of effective laws in the food industry. Extra virgin olive oil (EVOO) has many amazing benefits for human body's health. Due to the nutritional value and high price of EVOO, there is a lot of cheating in it. The ultrasound approach has many advantages in the food studies, and it is fast and nondestructive for quality evaluation. In this study, to fraud detection of EVOO four ultrasonic properties of oil in five levels of adulteration (5%, 10%, 20%, 35%, and 50%) were extracted. The 2 MHz ultrasonic probes were used in the DOI 1,000 STARMANS diagnostic ultrasonic device in a "probe holding mechanism." The four extracted ultrasonic features include the following: "percentage of amplitude reduction, time of flight (TOF), the difference between the first and second maximum amplitudes of the domain (in the time-amplitude diagram), and the ratio of the first and second maximum of amplitude." Seven classification algorithms including "Naïve Bayes, support vector machine, gradient boosting classifier, K-nearest neighbors, artificial neural network, logistic regression, and AdaBoost" were used to classify the preprocessed data. Results showed that the Naïve Bayes algorithm with 90.2% provided the highest accuracy among the others, and the support vector machine and gradient boosting classifier with 88.2% were in the next ranks.

Development of an electrochemical biosensor for impedimetric detection of tetracycline in milk.

ABSTRACT: This study dealt with the fabrication of an impedimetric biosensor based on nanomaterial modified with pencil graphite electrode for the detection of tetracycline (TET) in milk samples. For response of the impedimetric aptasensor to be improved, the influence of different parameters (immobilization time of reduced grapheme oxide, time of aptamer, and TET binding, and concentration of aptamer) was optimized. In optimum conditions, the aptasensor provided a concentration range within 1 × 10-16 - 1 × 10-6 M and with a limit of detection of 3 × 10-17 M TET. The proposed impedimetric aptasensor was then used in milk samples analysis, and the acceptable recovery was achieved ranging from 92.8 to 102.1%. According to this study, the combination of an aptamer and electrochemical impedance spectroscopy is a promising method for detection of TET in milk samples with high reproducibility and stability.

An impedimetric aptasensor for ultrasensitive detection of Penicillin G based on the use of reduced graphene oxide and gold nanoparticles.

The authors describe an impedimetric aptasensor for Penicillin G (PEN) which is an important antibiotic. The method is based on the use of a pencil graphite electrode (PGE) modified with reduced graphene oxide (RGO) and gold nanoparticles (GNPs) for ultrasensitive detection of PEN. The morphology of a bare PGE, RGO/PGE, and GNP/RGO/PGE, and the functional groups on graphene oxide (GO) and RGO were studied using scanning electron microscopy and Fourier transform infrared spectroscopy. Electrochemical impedance spectroscopy was used for detection of PEN by measuring the charge transfer resistance (Rct). Also, cyclic voltammetry was recorded at potential range of 0.30 to +0.70 V for PGE treatment. This aptamer-based assay has a wide linear range that extends from 1.0 fM to 10 µM, and a limit of detection as low as 0.8 fM. The method was applied to the determination of PEN in spiked milk from cow, sheep, goat and water buffalo. Recoveries ranged from 92% to 104%. The assay is fast, ultrasensitive, high reproducible, and selective over antibiotics such as streptomycin, tetracycline, and sulfadiazine. Graphical abstract Schematic presentation of an impedimetric aptasensor for Penicillin G antibiotic using a pencil graphite electrode (PGE) modified with reduced graphene oxide (RGO) and gold nanoparticles (GNPs). This aptamer based assay has limit of detection as low as 0.8 fM.

Differentiation of cumin seeds using a metal-oxide based gas sensor array in tandem with chemometric tools.

Cumin is a plant of the Apiaceae family (umbelliferae) which has been used since ancient times as a medicinal plant and as a spice. The difference in the percentage of aromatic compounds in cumin obtained from different locations has led to differentiation of some species of cumin from other species. The quality and price of cumin vary according to the specie and may be an incentive for the adulteration of high value samples with low quality cultivars. An electronic nose simulates the human olfactory sense by using an array of sensors to distinguish complex smells. This makes it an alternative for the identification and classification of cumin species. The data, however, may have a complex structure, difficult to interpret. Given this, chemometric tools can be used to manipulate data with two-dimensional structure (sensor responses in time) obtained by using electronic nose sensors. In this study, an electronic nose based on eight metal oxide semiconductor sensors (MOS) and 2D-LDA (two-dimensional linear discriminant analysis), U-PLS-DA (Partial least square discriminant analysis applied to the unfolded data) and PARAFAC-LDA (Parallel factor analysis with linear discriminant analysis) algorithms were used in order to identify and classify different varieties of both cultivated and wild black caraway and cumin. The proposed methodology presented a correct classification rate of 87.1% for PARAFAC-LDA and 100% for 2D-LDA and U-PLS-DA, indicating a promising strategy for the classification different varieties of cumin, caraway and other seeds.

Performance Comparison of Fuzzy ARTMAP and LDA in Qualitative Classification of Iranian Rosa damascena Essential Oils by an Electronic Nose.

Quality control of essential oils is an important topic in industrial processing of medicinal and aromatic plants. In this paper, the performance of Fuzzy Adaptive Resonant Theory Map (ARTMAP) and linear discriminant analysis (LDA) algorithms are compared in the specific task of quality classification of Rosa damascene essential oil samples (one of the most famous and valuable essential oils in the world) using an electronic nose (EN) system based on seven metal oxide semiconductor (MOS) sensors. First, with the aid of a GC-MS analysis, samples of Rosa damascene essential oils were classified into three different categories (low, middle, and high quality, classes C1, C2, and C3, respectively) based on the total percent of the most crucial qualitative compounds. An ad-hoc electronic nose (EN) system was implemented to sense the samples and acquire signals. Forty-nine features were extracted from the EN sensor matrix (seven parameters to describe each sensor curve response). The extracted features were ordered in relevance by the intra/inter variance criterion (Vr), also known as the Fisher discriminant. A leave-one-out cross validation technique was implemented for estimating the classification accuracy reached by both algorithms. Success rates were calculated using 10, 20, 30, and the entire selected features from the response of the sensor array. The results revealed a maximum classification accuracy of 99% when applying the Fuzzy ARTMAP algorithm and 82% for LDA, using the first 10 features in both cases. Further classification results explained that sub-optimal performance is likely to occur when all the response features are applied. It was found that an electronic nose system employing a Fuzzy ARTMAP classifier could become an accurate, easy, and inexpensive alternative tool for qualitative control in the production of Rosa damascene essential oil.

Screening analysis of beer ageing using near infrared spectroscopy and the Successive Projections Algorithm for variable selection.

This work proposes a method for monitoring the ageing of beer using near-infrared (NIR) spectroscopy and chemometrics classification tools. For this purpose, the Successive Projections Algorithm (SPA) is used to select spectral variables for construction of Linear Discriminant Analysis (LDA) classification models. A total of 83 alcoholic and non-alcoholic beer samples packaged in bottles and cans were examined. To simulate a long storage period, some of the samples were stored in an oven at 40°C, in the dark, during intervals of 10 and 20 days. The NIR spectrum of these samples in the range 12,500-5405 cm(-1) was then compared against those of the fresh samples. The results of a Principal Component Analysis (PCA) indicated that the alcoholic beer samples could be clearly discriminated with respect to ageing stage (fresh, 10-day or 20-day forced ageing). However, such discrimination was not apparent for the non-alcoholic samples. These findings were corroborated by a classification study using Soft Independent Modelling of Class Analogy (SIMCA). In contrast, the use of SPA-LDA provided good results for both types of beer (only one misclassified sample) by using a single wavenumber in each case, namely 5550 cm(-1) for non-alcoholic samples and 7228 cm(-1) for alcoholic samples.

Meat quality assessment by electronic nose (machine olfaction technology).

Over the last twenty years, newly developed chemical sensor systems (so called "electronic noses") have made odor analyses possible. These systems involve various types of electronic chemical gas sensors with partial specificity, as well as suitable statistical methods enabling the recognition of complex odors. As commercial instruments have become available, a substantial increase in research into the application of electronic noses in the evaluation of volatile compounds in food, cosmetic and other items of everyday life is observed. At present, the commercial gas sensor technologies comprise metal oxide semiconductors, metal oxide semiconductor field effect transistors, organic conducting polymers, and piezoelectric crystal sensors. Further sensors based on fibreoptic, electrochemical and bi-metal principles are still in the developmental stage. Statistical analysis techniques range from simple graphical evaluation to multivariate analysis such as artificial neural network and radial basis function. The introduction of electronic noses into the area of food is envisaged for quality control, process monitoring, freshness evaluation, shelf-life investigation and authenticity assessment. Considerable work has already been carried out on meat, grains, coffee, mushrooms, cheese, sugar, fish, beer and other beverages, as well as on the odor quality evaluation of food packaging material. This paper describes the applications of these systems for meat quality assessment, where fast detection methods are essential for appropriate product management. The results suggest the possibility of using this new technology in meat handling.