machine learning (5)

12366097699?profile=RESIZE_180x180A scientific paper entitled ”Authenticity Assessment of Ground Black Pepper by Combining Headspace Gas-Chromatography Ion Mobility Spectrometry and Machine Learning” has now been published in Food Research International (Elsevier journal) 

The study assessed a broad variety of authentic samples originating from eight countries and three continents. The method uses head-space gas-chromtaography ion mobility spectrometry (HS-HC-IMS), combined with machine learning. It requires no sample preparation and is rapid. In this proof-of-concept study, the methos successfully classified samples with an accuracy of >90% with a 95% level of confidence.

Access the paper for free until the end of March 2024.

Photo by Anas Alhajj on Unsplash

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10974233682?profile=RESIZE_400xThis study aims to determine volatile compounds in red wines of Zweigelt and Rondo varieties using HS-SPME/GC-MS (headspace solid-phase microextraction gas chromatography - mass spectrometry), and to find a marker and/or a classification model for the assessment of varietal authenticity. Wines were produced from the two varieties, and 67 volatile compounds were tentatively identified in the test wines. The relative concentrations of volatiles were used as an input data set, divided into two subsets (training and testing), to the support vector machine (SVM) and k-nearest neighbor (kNN) algorithms. A subset of 6 volatiles was chosen for evaluation, and it was found that 2 of the volatiles in particular gave an accuracy of 100%. One of the chosen volatiles in Zweigelt wines was absent from the Rondo wines. Further chemometric analysis identified two further volatiles of importance for SVM . The classification model approach needs expanding for mixed varieties, and when the same varietal wines are produced in different geographical locations.

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10882816084?profile=RESIZE_400xGiven the price differential between NFC (not from concentrate) and FC (from concentrate) orange juice, the vulnerability of substitution of NFC by FC orange juice makes it important to have methodology to distinguish between the two type of processed juice. In this study, the differential compounds between NFC and FC orange juice prepared from 5 different orange cultivars and 4 sterilisation methods were identified using untargeted screening by UPLC-QTOF-MS (Ultra Performance Liquid Chromatography - Quadrapole Time of Flight Mass Spectrometry) and machine learning. Combining principal component analysis and orthogonal projection to latent structures discriminant analysis, 11 differential compounds for NFC and FC orange juices discrimination were identified, and used to discriminate between NFC and FC juices using targeted LC-MS methodology.

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Here, a biomarker-free detection assay was developed using an optical nanosensor array to aid in the food safety of citrus juices.

Researchers have coupled machine learning capability of their computational process named algorithmically guided optical nanosensor selector (AGONS) with the fluorescence data collected using their nanosensor array, in a biomarker-free detection assay, to construct a predictive model for citrus juice authenticity. 

Over 707 measurements of pure and adulterated citrus juices were collected for prediction. Overall, the approach achieved above 90% accuracy on three data sets in discriminating three pure citrus fruit juices, artificially sweetened tangerine juice with various concentrations of corn syrup, and juice-to-juice dilution of orange juice using apple juice. 

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9405001068?profile=RESIZE_584xA new environmentally friendly prototype sensor has been developed by CSIRO, Australia's national science agency, to help combat food-fraud and protect the reputation of Australian produce.

The novel technology uses vibration energy harvesting and machine learning to accurately detect anomalies in the transportation of products such as meat. 

For example, if a refrigeration truck carrying exported meat stopped during its journey to the processing plant, the technology would be able to detect this and if any products had been moved or removed during this period.

This allows producers and logistics operators to pin-point handling errors and identify when products are stolen or substituted.

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