New Zealand honey botanical origin classification with hyperspectral imaging

AI and CV based 2D-CNN algorithm: botanical authentication of Indian honey

“…A similar study was conducted by Zhang on New Zealand honey. 26 The results of CNN classication using a dataset generated from The validation accuracy of the model was 0.84, which is lower than our proposed model. 28 In a previous study, Shaee et al 29 proposed a method using hyperspectral imaging (HIS) technology to determine honey adulteration; HIS data is used to predict honey adulteration using the ANN model, with a model accuracy of 95%.…”

Hyperspectral Microscopy Technology to Detect Syrups Adulteration of Endemic Guindo Santo and Quillay Honey Using Machine-Learning Tools

“…Our results are justified not only in the context of endemic honey characterization but also in the benefit of hyperspectral microscopy. A very recent work published by Zhang et al in 2022 [ 40 ] compares different classifiers performance to categorize honey samples from New Zealand. In that work, botanical origin was categorized using four different machine learning algorithms, showing that SVM achieved >99% in accuracy rate, which is in agreement with our results.…”

Recent developments of e-sensing devices coupled to data processing techniques in food quality evaluation: a critical review

“…147 For the purpose of identifying honey botanical origins, random forest (RF) and SVM were coupled to hyperspectral imaging achieving more than 98% and 99% accuracy rates, respectively, and correctly discriminating between the various studied honey samples. 156 In the framework of the occurrence of adulteration fatal practices, Siamese networks were coupled to a computer vision system to trace the addition of papaya seeds in black peppercorns. Herewith, the constructed model yielded encouraging outcomes with a training and validation accuracy of 0.96 and 0.92, respectively.…”

“…Another category of food products prone to be evaluated by these systems were nuts, grains, beans, seeds such as chia seeds, 152 soybeans, 153 cocoa beans. 154 oils, 152,155 honeys, 156 and fish meat such as salmon fillets, 157 and dairy products punctuated by the evaluation of fresh cheese 158 and hard cheese 159 as well as beverages such as aged wines, 160 beers 161 and tequilas 162 were less likely to be evaluated by CVSs. Table 6 summarizes all the food matrices that were subjected to an evaluation as per e-eyes.…”