Authenticity markers of cultivated blueberries (Vaccinium sp.) and European wild blueberry (V. myrtillus) assessed by hyperspectral imaging and machine learning algorithms

• We recognized food fraud of wild blueberry from the cultivated counterparts. • We tested 190 822 individual models and 54 137 predictor combinations. • Food fraud of the presented fruit species and origins seems to be highly detectable. 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. 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. Moreover, 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). Also the rbf-SVM 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.

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Publication Details

Journal
Applied Food Research
Published
2024-11-05
DOI
https://doi.org/10.1016/j.afres.2024.100597
Citations
3
Primary Topic
Horticultural and Viticultural Research
Type
article
Field-Weighted Citation Impact
1.80
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article

Authenticity markers of cultivated blueberries (Vaccinium sp.) and European wild blueberry (V. myrtillus) assessed by hyperspectral imaging and machine learning algorithms

Jouni Karhu, Saila Karhu, Anna‐Liisa Välimaa, Pekka Jounela
3 citations
Applied Food Research
Horticultural and Viticultural Research
1.80
article

Authenticity markers of cultivated blueberries (Vaccinium sp.) and European wild blueberry (V. myrtillus) assessed by hyperspectral imaging and machine learning algorithms

Jouni Karhu, Saila Karhu, Anna‐Liisa Välimaa, Pekka Jounela
article en
3 citations

Abstract

• We recognized food fraud of wild blueberry from the cultivated counterparts. • We tested 190 822 individual models and 54 137 predictor combinations. • Food fraud of the presented fruit species and origins seems to be highly detectable. 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. 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. Moreover, 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). Also the rbf-SVM 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.

Applied Food ResearchVol. 4(2)
Natural Resources Institute Finland (FI)
Zero hunger
Openalex Percentile: Top 13%
Horticultural and Viticultural Research
1.80
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