Authenticity markers of cultivated blueberries (Vaccinium sp.) and European wild blueberry (V. myrtillus) assessed by hyperspectral imaging and machine learning algorithms
Year: 2024
Type: article
Abstract: The present study was undertaken to tackle food fraud in the food chain by developing a novel panel of analytical fingerprints to distinguish the fruit of bilberry (Vaccinium myrtillus), a wild European blueberry grown in Finland, from their commercial cultivated counterparts based on V. corymbosum or other originally American Vaccinium species. The analytical fingerprints were derived using hyperspectral imaging (HSI). The cultivated domestic blueberry samples from Finland (N = 188), those imported (N = 338), and domestic Finnish wild bilberry fruit (N = 247) were imaged using Specim IQ and FX17 HSI cameras, resulting in 204 and 224 wavelength predictors in 3 nm intervals, between 397 and 1,003 nm and 935 and 1,720 nm respectively. The fruit HSI data were then classified using seven machine learning model types. The supervised evolutionary feature selection algorithm was used to recognize the most separating subset of wavelengths (nm predictors, colors) between the fruit class labels, which were 1) wild bilberry fruit vs. cultivated blueberry fruit and 2) domestic cultivated blueberry fruit vs. imported cultivated blueberry fruit. We ran 190,822 individual models and 54,137 predictor combinations to determine whether subsets or all wavelengths better distinguished fruit class labels. Overall, the best model type, the rbf kernel support vector machine (rbf-SVM), showed excellent prediction performance when classifying wild bilberries vs. cultivated blueberries with HSI data obtained using both camera types (4.1% and 3.2% holdout set errors). The rbf-SVM also performed well when classifying domestic cultivated blueberries vs. those imported with HSI data obtained using an IQ camera (2.3% holdout set error). Our results showed that hyperspectral imaging could predict food fraud attempts concerning the Vaccinium fruit of different origin and species.
Source: Applied Food Research
Institution: Natural Resources Institute Finland
Language: English
Subfield: Analytical Chemistry
Field: Chemistry
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $1,132
Funder: Luonnonvarakeskus
OpenAlex ID: W4404028938