Evaluation of a dual-probe real time PCR system for detection of mandarin in commercial orange juice

Rapid Detection and Quantification of Adulterants in Fruit Juices Using Machine Learning Tools and Spectroscopy Data

Fruit juice production is one of the most important sectors in the beverage industry, and its adulteration by adding cheaper juices is very common. This study presents a methodology based on the combination of machine learning models and near-infrared spectroscopy for the detection and quantification of juice-to-juice adulteration. We evaluated 100% squeezed apple, pineapple, and orange juices, which were adulterated with grape juice at different percentages (5%, 10%, 15%, 20%, 30%, 40%, and 50%). The spectroscopic data have been combined with different machine learning tools to develop predictive models for the control of the juice quality. The use of non-supervised techniques, specifically model-based clustering, revealed a grouping trend of the samples depending on the type of juice. The use of supervised techniques such as random forest and linear discriminant analysis models has allowed for the detection of the adulterated samples with an accuracy of 98% in the test set. In addition, a Boruta algorithm was applied which selected 89 variables as significant for adulterant quantification, and support vector regression achieved a regression coefficient of 0.989 and a root mean squared error of 1.683 in the test set. These results show the suitability of the machine learning tools combined with spectroscopic data as a screening method for the quality control of fruit juices. In addition, a prototype application has been developed to share the models with other users and facilitate the detection and quantification of adulteration in juices.

A review of fruit juice authenticity assessments: Targeted and untargeted analyses

Fruit juices are becoming more and more popular in the whole world. However, the increasing fruit juice fraud cases are undermining the healthy development of fruit juice industry. Fruit juice authenticity represents an important food quality and safety parameter. Many techniques have been applied in fruit juices authenticity assessment. The purpose of this review is to provide a research overview of the targeted and untargeted analyses of fruit authentication, and a method selection guide for fruit juice authenticity assessment. Targeted markers, such as stable isotopes, phenolics, carbohydrates, organic acids, volatile components, DNAs, amino acids and proteins, as well as carotenoids, will be discussed. And untargeted techniques, including liquid/gas chromatography-mass spectrometer, nuclear magnetic resonance, infrared spectroscopy, inductively-coupled plasma-mass spectrometry/optical emission spectrometer, fluorescence spectra, electronic sensors and others, will be reviewed. The emerging untargeted for novel targeted marker analysis will be also summarized.

Determination of the geographical origin of maize (Zea mays L.) using mineral element fingerprints

BACKGROUND

Maize (Zea mays L.) is a staple cereal crop and feed crop throughout the world. In this article, a mineral element fingerprinting technique was applied to single out suitable element indicators to determine the geographical origin of maize. A total of 90 fresh maize samples were collected in 2107 from Jilin, Gansu, and Shandong provinces in China. The contents of 25 mineral elements in all maize samples were measured by inductively coupled plasma mass spectrometry (ICP-MS). The composition of mineral elements was analyzed by multivariate statistical analysis, including one-way analysis of variance (one-way ANOVA), principal component analysis (PCA), k-nearest neighbor (KNN) analysis, and stepwise linear discriminant analysis (SLDA).

RESULTS

As compared by one-way ANOVA, the contents of 19 mineral elements in maize samples were significantly different among three provinces. Principal component analysis based on these 19 elements could obtain preliminary visual classification groups of maize samples. K-nearest neighbor analysis produced a total correct classification rate of 83.9% on the training set, and 82.2% on the prediction set. The SLDA model, based on eight indicative elements (Na, Cr, Rb, Sr, Mo, Cs, Ba, and Pb) obtained a total correct classification rate of 92.2% with cross-validation.

CONCLUSION

The mineral element fingerprinting technique combined with multivariate statistical analysis could be a helpful method to identify the geographical origin of maize. © 2019 Society of Chemical Industry.

Quality and Authenticity Control of Fruit Juices-A Review

Food fraud, being the act of intentional adulteration of food for financial advantage, has vexed the consumers and the food industry throughout history. According to the European Committee on the Environment, Public Health and Food Safety, fruit juices are included in the top 10 food products that are most at risk of food fraud. Therefore, reliable, efficient, sensitive and cost-effective analytical methodologies need to be developed continuously to guarantee fruit juice quality and safety. This review covers the latest advances in the past ten years concerning the targeted and non-targeted methodologies that have been developed to assure fruit juice authenticity and to preclude adulteration. Emphasis is placed on the use of hyphenated techniques and on the constantly-growing role of MS-based metabolomics in fruit juice quality control area.

Novel analytical method for detection of orange juice adulteration based on ultra-fast gas chromatography

ABSTRACT

The food authenticity assessment is an increasingly important issue in food quality and safety. The application of an electronic nose based on ultra-fast gas chromatography technique enables rapid analysis of the volatile compounds from food samples. Due to the fact that this technique provides chemical profiling of natural products, it can be a powerful tool for authentication in combination with chemometrics. In this article, a methodology for classification of Not From Concentrate (NFC) juices was presented. During research samples of 100% orange juice, 100% apple juice, as well as mixtures of these juices with known percentage of base juices were tested. Classification of juice samples was carried out using unsupervised and supervised statistical methods. As chemometric methods, Hierarchical Cluster Analysis, Classification Tree, Naïve Bayes, Neural Network, and Random Forest classifiers were used. The ultra-fast GC technique coupled with supervised statistical methods allowed to distinguish juice samples containing only 1.0% of impurities. The developed methodology is a promising analytical tool to ensure the authenticity and good quality of juices.

GRAPHICAL ABSTRACT

Variable selection for multivariate classification aiming to detect individual adulterants and their blends in grape nectars

During the quality inspection control of fruit beverages, some types of adulterations can be detected, such as the addition or substitution with less expensive fruits. To determine whether grape nectars were adulterated by substitution with apple or cashew juice or by a mixture of both, a methodology based on attenuated total reflectance Fourier transform mid infrared spectroscopy (ATR-FTIR) and multivariate classification methods was proposed. Partial least squares discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA) models were developed as multi-class methods (classes unadulterated, adulterated with cashew and adulterated with apple) with the full-spectra. PLS-DA presented better performance parameters than SIMCA in the classification of samples with just one adulterant, while poor results were achieved for samples with blends of two adulterants when using both classification methods. Three variable selection methods were tested in order to improve the effectiveness of the classification models: interval partial least squares (iPLS), variable importance in projection scores (VIP scores) and a genetic algorithm (GA). Variable selection methods improved the performance parameters for the SIMCA and PLS-DA methods when they were used to predict samples with only one adulterant. Only PLS-DA coupled with iPLS was able to classify samples with blends of two adulterants, providing sensitivity values between 100% and 83% at 100% specificity for the three studied classes.

Determination of main fruits in adulterated nectars by ATR-FTIR spectroscopy combined with multivariate calibration and variable selection methods

Grape, orange, peach and passion fruit nectars were formulated and adulterated by dilution with syrup, apple and cashew juices at 10 levels for each adulterant. Attenuated total reflectance Fourier transform mid infrared (ATR-FTIR) spectra were obtained. Partial least squares (PLS) multivariate calibration models allied to different variable selection methods, such as interval partial least squares (iPLS), ordered predictors selection (OPS) and genetic algorithm (GA), were used to quantify the main fruits. PLS improved by iPLS-OPS variable selection showed the highest predictive capacity to quantify the main fruit contents. The selected variables in the final models varied from 72 to 100; the root mean square errors of prediction were estimated from 0.5 to 2.6%; the correlation coefficients of prediction ranged from 0.948 to 0.990; and, the mean relative errors of prediction varied from 3.0 to 6.7%. All of the developed models were validated.

Poisonous or non-poisonous plants? DNA-based tools and applications for accurate identification

Plant exposures are among the most frequently reported cases to poison control centres worldwide. This is a growing condition due to recent societal trends oriented towards the consumption of wild plants as food, cosmetics, or medicine. At least three general causes of plant poisoning can be identified: plant misidentification, introduction of new plant-based supplements and medicines with no controls about their safety, and the lack of regulation for the trading of herbal and phytochemical products. Moreover, an efficient screening for the occurrence of plants poisonous to humans is also desirable at the different stages of the food supply chain: from the raw material to the final transformed product. A rapid diagnosis of intoxication cases is necessary in order to provide the most reliable treatment. However, a precise taxonomic characterization of the ingested species is often challenging. In this review, we provide an overview of the emerging DNA-based tools and technologies to address the issue of poisonous plant identification. Specifically, classic DNA barcoding and its applications using High Resolution Melting (Bar-HRM) ensure high universality and rapid response respectively, whereas High Throughput Sequencing techniques (HTS) provide a complete characterization of plant residues in complex matrices. The pros and cons of each approach have been evaluated with the final aim of proposing a general user's guide to molecular identification directed to different stakeholder categories interested in the diagnostics of poisonous plants.

Controlling protected designation of origin of wine by Raman spectroscopy

In this paper, a Fourier Transform Raman spectroscopy method, to authenticate the provenience of wine, for food traceability applications was developed. In particular, due to the specific chemical fingerprint of the Raman spectrum, it was possible to discriminate different wines produced in the Piedmont area (North West Italy) in accordance with i) grape varieties, ii) production area and iii) ageing time. In order to create a consistent training set, more than 300 samples from tens of different producers were analyzed, and a chemometric treatment of raw spectra was applied. A discriminant analysis method was employed in the classification procedures, providing a classification capability (percentage of correct answers) of 90% for validation of grape analysis and geographical area provenance, and a classification capability of 84% for ageing time classification. The present methodology was applied successfully to raw materials without any preliminary treatment of the sample, providing a response in a very short time.