Integration of handheld NIR and machine learning to “Measure & Monitor” chicken meat authenticity

By combining portable, handheld near-infrared (NIR) spectroscopy with state-of-the-art classification algorithms, we developed a powerful method to test chicken meat authenticity. The research presented shows that it is both possible to discriminate fresh from thawed meat, based on NIR spectra, as well as to correctly classify chicken fillets according to the growth conditions of the chickens with good accuracy. In all cases, the random subspace discriminant ensemble (RSDE) method significantly outperformed other common classification methods such as partial least squares-discriminant analysis (PLS-DA), artificial neural network (ANN) and support vector machine (SVM) with classification accuracy of >95%. This study shows that handheld NIR coupled with machine learning algorithms is a useful, fast, non-destructive tool to identify the authenticity of chicken meat. By comparing and combining different protocols to measure the NIR spectra (i.e., through packaging and directly on meat), we show the possibilities for both consumers and food inspection authorities to check the authenticity and origin of packaged chicken fillet.</p

Linking sensory and proton transfer reaction–mass spectrometry analyses for the assessment of melon fruit (Cucumis melo L.) quality traits

Sixty-seven samples of ten melon types (Cucumis melo L.) were evaluated to determine the relationship between their quality traits: sensory attributes, pH, soluble solids, and volatile organic compounds. Fruits from the cantalupensis, conomon, dudaim, inodorus, and momordica cultivar groups were analyzed. The sensory profiles were assessed using ten attributes covering odor, flavor, and taste characteristics, whereas the volatile profiles were derived by proton transfer reaction–mass spectrometry. Fruits from the cantalupensis and inodorus cultivars showed an opposite pattern for several quality traits. Fruits from the dudaim cultivar were more related to the cantalupensis, whereas conomon and momordica showed an intermediate behavior between inodorus and cantalupensis. The attributes of odor and flavor intensity, ripe fruit odor, fermentative odor, and fermentative flavor correlated positively to C3–C9 esters (r = 0.43–0.73; p ≤ 0.01). Positive correlations were also observed for several alcohols (r = 0.36–0.82; p ≤ 0.05), including methanol, ethanol, and diol alcohols, as well as for several aldehydes (r = 0.43–0.85; p ≤ 0.01), such as acetaldehyde, butanal, methyl butanal, heptanal, and decanal. The attributes mentioned above were negatively correlated with two C9 aldehydes, 2,6-nonadienal and nonenal (r = − 0.45 to − 0.62; p ≤ 0.01), whereas sweetness was negatively correlated with two C6 green leaf volatiles, hexenal and 3-hexenol (r = − 0.50; − 0.67; p ≤ 0.001). The melon fruits presented distinct differences in the quality traits evaluated. These results provide information for the development of new cultivars with characteristic taste combinations without compromising other desirable fruit quality traits.info:eu-repo/semantics/acceptedVersio

Data of: Integration of handheld NIR and machine learning for the development of a “Measure & Monitor” technology for chicken meat authenticity

Near Infrared spectroscopy data of chicken meat (fillets) from multiple growth conditions and countries of origin (Netherlands and Ireland). The data was recorded by applying a MicroNIR Pro (Viavi) device equipped with the standard issue collar by applying on the samples in three different ways: (i) directly on the meat, (ii) through the top foil of the package (i.e. with an air pocket between the foil and the breast fillet) and (iii) through the top foil packaging bottom up (i.e. no air pocket between the foil and the breast fillet). Five replicates were recorded per sample

Importance of harmonised sample preparation for moisture and protein content determinations in official food control laboratories: A poultry meat case study

Commission Regulation (EC) 543/2008 limits moisture and protein contents in poultry meat. However, this regulation leaves room for interpretation regarding sample homogenisation, potentially affecting comparability of laboratory results. Therefore, a proficiency test and sample homogenisation study were organised amongst 19 European National Reference Laboratories (NRL). In the proficiency test, three different pre-homogenised chicken samples (fillets, drumsticks and carcasses) were analysed. Only one NRL produced unsatisfactory results. In the homogenisation study, NRLs were supplied with uniform fillet, drumstick and carcass materials. Homogenisation was performed according to the NRLs in-house methods. Five NRLs did not return satisfactory results. As these NRLs produced satisfactory results in the proficiency test, their increase in z-scores was related to their homogenisation practices. Overall, scattering of individual results was higher for drumsticks compared to fillets and carcasses. Homogenisation practices for poultry meat introduced significant differences in moisture and protein results and standardisation is therefore advisable.</p

Data of: Integration of handheld NIR and machine learning for the development of a “Measure & Monitor” technology for chicken meat authenticity

Near Infrared spectroscopy data of chicken meat (fillets) from multiple growth conditions and countries of origin (Netherlands and Ireland). The data was recorded by applying a MicroNIR Pro (Viavi) device equipped with the standard issue collar by applying on the samples in three different ways: (i) directly on the meat, (ii) through the top foil of the package (i.e. with an air pocket between the foil and the breast fillet) and (iii) through the top foil packaging bottom up (i.e. no air pocket between the foil and the breast fillet). Five replicates were recorded per sample

Detecting fraudulent additions in skimmed milk powder using a portable, hyphenated, optical multi-sensor approach in combination with one-class classification

The detection of fraudulent additions to milk powder is an ongoing research subject for governmental agencies, industry and academia. Current developments steer towards the application of so-called fingerprint approaches, describing authentic, reference samples with spectroscopy and using one-class classification (OCC) to identify “out-of-class”, or adulterated samples. Within this article we describe the application of a novel, portable device hyphenating ultraviolet–visible, fluorescence and near-infrared spectroscopy in combination with OCC modelling to discriminate authentic skimmed milk powders from adulterated ones. As adulterated samples we analyzed skimmed milk powder with the addition of plant protein powder, whey powder, starch, lactose, glucose, fructose as well as non-protein nitrogen like ammonium chloride, ammonium nitrate, melamine and urea in different concentrations. After fusion of the classification results from the three spectral techniques and several models two scenarios are presented. 100% (scenario 1) or 80% (scenario 2) of the authentic skimmed milk powders were correctly identified as “in-class”, whereas respectively 64% or 86% of the adulterated samples were correctly classified as “out-of-class”. In brief, this article provides insights in the application of novel, portable devices that may be applied in a non-invasive manner and gives an outlook on data handling and a new data fusion strategy.</p

Detecting Food Fraud in Extra Virgin Olive Oil Using a Prototype Portable Hyphenated Photonics Sensor

BackgroundCurrent developments in portable photonic devices for fast authentication of extra virgin olive oil (EVOO) or EVOO with non-EVOO additions steer towards hyphenation of different optic technologies. The multiple spectra or so-called “fingerprints” of samples are then analyzed with multivariate statistics. For EVOO authentication, one-class classification (OCC) to identify “out-of-class” EVOO samples in combination with data-fusion is applicable.ObjectiveProspecting the application of a prototype photonic device (“PhasmaFood”) which hyphenates visible, fluorescence, and near-infrared spectroscopy in combination with OCC modelling to classify EVOOs and discriminate them from other edible oils and adulterated EVOOs.MethodEVOOs were adulterated by mixing in 10–50% (v/v) of refined and virgin olive oils, olive-pomace olive oils, and other common edible oils. Samples were analyzed by the hyphenated sensor. OCC, data-fusion, and decision thresholds were applied and optimized for two different scenarios.ResultsBy high-level data-fusion of the classification results from the three spectral databases and several multivariate model vectors, a 100% correct classification of all pure edible oils using OCC in the first scenario was found. Reducing samples being falsely classified as EVOOs in a second scenario, 97% of EVOOs adulterated with non-EVOO olive oils were correctly identified and ones with other edible oils correctly classified at score of 91%.ConclusionsPhotonic sensor hyphenation in combination with high-level data fusion, OCC, and tuned decision thresholds delivers significantly better screening results for EVOO compared to individual sensor results.HighlightsHyphenated photonics and its data handling solutions applied to extra virgin olive oil authenticity testing was found to be promising

New approaches towards discrimination of fresh/chilled and frozen/thawed chicken breasts by HADH activity determination : Customized slope fitting and chemometrics

Fresh/chilled chicken breasts retail at a higher price than their frozen/thawed counterparts. Verification of the fresh/thawed status of chicken meat is determined by measuring β-hydroxyacyl-Coenzyme A-hydrogenase (HADH) activity present in meat intra-cellular liquids spectrophotometrically. However, considerable numbers of reference samples are required for the current arithmetic method, adding to laboratory costs. Therefore, two alternative mathematical approaches which do not require such reference samples were developed and evaluated: curve fitting and multivariate classification. The approaches were developed using 55 fresh/thawed fillet samples. The performance of the methods was examined by an independent validation set which consisted of 16 samples. Finally, the approach was tested in practice in a market study. With the exception of two minor false classifications, both newly proposed methods performed equally well as the classical method. All three methods were able to identify two apparent fraudulent cases in the market study. Therefore, the experiments showed that the costs of HADH measurements can be reduced by adapting alternative mathematics

A multi-analyte screening method for the rapid detection of illicit adulterants in dietary supplements using a portable SERS analyzer

The popularity and number of dietary supplements on the health market have experienced an unprecedented boost in recent years. Simultaneously, their increased use has been accompanied by an increase in acute intoxication cases linked to the adulteration of these products with illicit and undeclared substances. In this study, a SERS-based screening methodology was developed to rapidly detect illegally added pharmaceutically active substances to dietary supplements. A portable analyzer and silver printed-SERS substrates were used to enhance the signal, requiring less than 20 min of sample preparation prior to the analysis. The method was successful in the qualitative identification of eleven out of twenty-three illicit adulterants in the dietary supple-ments; it could detect the target compounds at realistic adulteration levels (0.1-5.0% w/w), demonstrating the potential of SERS-based methodologies for forensic rapid screening applica-tions. The developed method is quick, easy to use, requires no skilled technicians and little sample preparation, and allows in-situ analyses. For these reasons, it is suitable for quick screening to be performed by inspectors at customs. Moreover, the low specificity of spectroscopic methods, to which SERS belongs, would benefit the detection of newly synthesized analogues of the target adulterants, which would otherwise be more difficult using common mass spectrometry methods in absence of reference standards

Protocatechuic Acid Levels Discriminate Between Organic and Conventional Wheat from Denmark

Organic wheat retails at higher market prices than the conventionally grown counterparts. In view of fair competition and sustainable consumer confidence, the organic nature of organic wheat needs to be assured. Amongst other controls this requires analytical tests based on discriminating traits. In this paper, phenolic acids were examined by liquid chromatography analysis as biomarkers for discriminating between the two groups by means of a controlled grown full factorial design Danish wheat sample set. By combining baseline and retention-time correction pre-treatments and principal component analysis, discrimination between organic and conventional produce was found to be expressed in the first principal component (93%), whilst the second principal component accounted for the production year (4%). Upon examination of the loadings plot, a single chromatographic peak was found to account for a large part in the discrimination between the two wheat production systems. This was further underpinned by statistically significant differences found in concentrations between the organic and conventional production systems of this phenolic acid (ANOVA,