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Seed fraud, particularly the misrepresentation of rice paddy (unhusked rice grain) as rice seed, is a growing concern that threatens sustainability efforts.

This study (open access) proves the concept of using a portable NIR spectroscopic device, combined with chemometric analysis, for rapid onsite identification of rice seed and paddy varieties for real-time verification of seed authenticity.

A total of 280 rice samples, representing four varieties (Agra, Amankwatia, Legon 1, and Jasmine 85) across two categories (seeds and paddy), were analyzed.

After applying various pre-processing techniques and principal component analysis (PCA), the authors report that linear discriminant functions 1 and 2 revealed distinct clustering patterns for both the varieties and categories (rice seed and paddy). Among the classification algorithms used, Random Forest (RF) achieved 100 % accuracy for rice seed identification and 97.38 % for paddy identification in the test sets. Support Vector Machine (SVM) demonstrated 98.15 % accuracy in distinguishing between rice seed and paddy for detecting seed fraud.

The authors conclude that such a portable NIR device can reliably perform varietal identification and seed authenticity checks, including use by seed inspectors, farmers, and regulatory officers.

Photo by Prahlad Inala on Unsplash

Dehydrated bee pollen is a premium product with a growing market as a food supplement, functional food ingredient, and is also an ingredient in biomedical and healthcare formulations.  Pollen from certain species of stingless bees is higher priced than the equivalent from the much more common honey bee, as the former is considered to have enhanced nutritional and health benefits.  Pollens from different bee species are visually identical, giving an incentive for potential fraud.

In this paper (purchase required) the authors built a classification model to discriminate pollens from different bee species.  The researchers integrated digital image processing and machine learning to classify pollen loads produced by honey bees (Apis mellifera) and pot-pollen from stingless bee species based on their colour patterns. A total of 246 pollen loads and pot-pollen samples from five bee species (Apis mellifera, Melipona marginata, Melipona quadrifasciata quadrifasciata, Scaptotrigona bipunctata, and Tetragona clavipes) were collected, and high-resolution images were captured using a smartphone. Colour parameters extracted from images such as R, B, H, and V were analyzed, and classification models employing CatBoost, XGBoost, Random Forest, and k-Nearest Neighbors (kNN) algorithms were tested.

They reported that CatBoost achieved the highest performance, with accuracies of 100 % in the training phase and 98.9 % in the testing phase. Linear Discriminant Analysis (LDA) further validated the classification by grouping the pollen samples into five distinct clusters corresponding to the bee species studied.

They conclude that combining digital image processing from a smartphone with machine learning offers an effective approach to classifying pollen from different bee species, with promising applications in apiculture to ensure product quality and authenticity.

Photo by Aaron Burden on Unsplash

Michel Neilsen, professor of analytical chemistry at Wageningen University & Research, was recently interviewed about an EU-funded project to develop smartphone-based food screening capabilities.

The FoodSmartphone project aims to develop smartphone-based (bio)analytical sensing and diagnostics tools for simplified on-site rapid pre-screening of food. The three-year project is scheduled to wrap up in December.

Nielsen explains that the project works to develop a device that can be attached or connected to a shopper's smartphone to test for allergens, pesticides, and whether the product is organic. The team hopes to empower shoppers to test food at the shelf.

The interview was recently published by Horizon, the EU Research and Innovation magazine from the European Commission, and can be read here.