Honey botanical origin classification using hyperspectral imaging and machine learning

Noviyanto, Ary; Abdulla, Waleed H.

doi:10.1016/j.jfoodeng.2019.109684

Cited by 43 publications

(23 citation statements)

References 163 publications

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“…In addition, deep learning models usually reply on heavy computation source and long training time, which seem inefficient for our specific problem. This may also explain why many food inspection works [17,18,[20][21][22] prefer to use conventional machine learning models rather than deep learning. 2) After introducing the background as another class per your constructive suggestion, more misclassification cases occur inevitably, leading to degraded OA, KP and average accuracy (AA) of our proposed method which are reduced from 98.32%, 98.26% and 98.32% to 97.32%, 95.86% and 93.73%, respectively.…”

Section: ) Extended Experiments On Background Analysismentioning

confidence: 99%

“…In recent years, a wide range of machine learning algorithms have been successfully applied for HSI based food quality inspection and grading applications. Noviyanto et al [17] proposed a multi-stage model including noisy band elimination, spectral normalization and hierarchical classification, to classify the honey botanical origin with the k-nearest neighbor (KNN) and support vector machine (SVM) classifiers. Erkinbaev et al [18] built an artificial neural network (ANN) to predict wheat hardness from HSI data.…”

Section: Introductionmentioning

confidence: 99%

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Nondestructive Phenolic Compounds Measurement and Origin Discrimination of Peated Barley Malt Using Near-Infrared Hyperspectral Imagery and Machine Learning

Yan

Ren

Tschannerl

et al. 2021

IEEE Trans. Instrum. Meas.

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Section: ) Extended Experiments On Background Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nondestructive Phenolic Compounds Measurement and Origin Discrimination of Peated Barley Malt Using Near-Infrared Hyperspectral Imagery and Machine Learning

Yan

Ren

Tschannerl

et al. 2021

IEEE Trans. Instrum. Meas.

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“…A promising quick, automatic, and non-invasive approach for honey botanical origin classification was developed using a combination of VIS/NIR hyperspectral imaging and machine learning, namely SVM and k-NN [24]. The developed techniques include noisy band elimination, spectral normalization, and hierarchical classification.…”

Section: Honey and Other Products Of Animal Originmentioning

confidence: 99%

“…The number of scientific works regarding the use of spectroscopy for food authenticity increased from 134 papers during 2010–2014 to 369 papers during 2015–2019 ( Figure 3 a), while the number of total citations ( Figure 3 b) doubled during the last five years (20,784 citations between 2015 and 2019 versus 9666 citations between 2010 and 2014). Some examples of recent applications of spectroscopic techniques for authentication of food products of animal origin include detection of adulteration in meat [ 14 , 15 ] identification of milk species [ 16 , 17 ], detection of thawed muscle foods [ 18 , 19 ] identification of muscle foods species [ 20 , 21 , 22 ], and determination of the botanical origin of honey [ 23 , 24 ], among many others.…”

Section: Introductionmentioning

confidence: 99%

Fraud in Animal Origin Food Products: Advances in Emerging Spectroscopic Detection Methods over the Past Five Years

Hassoun

Måge

Schmidt

et al. 2020

Foods

102

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Animal origin food products, including fish and seafood, meat and poultry, milk and dairy foods, and other related products play significant roles in human nutrition. However, fraud in this food sector frequently occurs, leading to negative economic impacts on consumers and potential risks to public health and the environment. Therefore, the development of analytical techniques that can rapidly detect fraud and verify the authenticity of such products is of paramount importance. Traditionally, a wide variety of targeted approaches, such as chemical, chromatographic, molecular, and protein-based techniques, among others, have been frequently used to identify animal species, production methods, provenance, and processing of food products. Although these conventional methods are accurate and reliable, they are destructive, time-consuming, and can only be employed at the laboratory scale. On the contrary, alternative methods based mainly on spectroscopy have emerged in recent years as invaluable tools to overcome most of the limitations associated with traditional measurements. The number of scientific studies reporting on various authenticity issues investigated by vibrational spectroscopy, nuclear magnetic resonance, and fluorescence spectroscopy has increased substantially over the past few years, indicating the tremendous potential of these techniques in the fight against food fraud. It is the aim of the present manuscript to review the state-of-the-art research advances since 2015 regarding the use of analytical methods applied to detect fraud in food products of animal origin, with particular attention paid to spectroscopic measurements coupled with chemometric analysis. The opportunities and challenges surrounding the use of spectroscopic techniques and possible future directions will also be discussed.

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“…e results showed that the method was effective on melon's cracking identification. Noviyanto and Abdulla [43] reported the use of similar classification algorithm called the k nearest neighbor (kNN) clustering to classify honey botanical origin with around 83% accuracy and 2.6% standard deviation. Cordella et al [18] investigated indirect honey adulteration from 10 to 40% using several bee-feeding sugar syrups.…”

Section: K-means Clusteringmentioning

confidence: 99%

Classification and Prediction of Bee Honey Indirect Adulteration Using Physiochemical Properties Coupled with K-Means Clustering and Simulated Annealing-Artificial Neural Networks (SA-ANNs)

Al-Mahasneh

Alu’datt

Rababah

et al. 2021

Journal of Food Quality

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The higher demand and limited availability of honey led to different forms of honey adulteration. Honey adulteration is either direct by addition of various syrups to natural honey or indirect by feeding honey bees with sugar syrups. Therefore, a need has emerged for reliable and cost-effective quality control methods to detect honey adulteration in order to ensure both safety and quality of honey. In this study, honey is adulterated by feeding honey bees with various proportions of sucrose syrup (0 to 100%). Various physiochemical properties of the adulterated honey are studied including sugar profile, pH, acidity, moisture, and color. The results showed that increasing sucrose syrup in the feed resulted in a decrease in glucose and fructose contents significantly, from 33.4 to 29.1% and 45.2 to 35.9%, respectively. Sucrose content, however, increased significantly from 0.19 to 1.8%. The pH value increased significantly from 3.04 to 4.63 with increase in sucrose feed. Acidity decreased slightly but nonsignificantly with increase in sucrose feed and varied between 7.0 and 4.00 meq/kg for 0% and 100% sucrose, respectively. Honey’s lightness (L value) also increased significantly from 59.3 to 68.84 as sucrose feed increased. Other color parameters were not significantly changed by sucrose feed. K-means clustering is used to classify the level of honey adulteration by using the above physiological properties. The classification results showed that both glucose content and total sugar content provided 100% accurate classification while pH values provided the worst results with 52% classification accuracy. To further predict the percent honey adulteration, simulated annealing coupled with artificial neural networks (SA-ANNs) was used with sugar profile as an input. RBF-ANN was found to provide the best prediction results with SSE = 0.073, RE = 0.021, and overall R2 = 0.992. It is concluded that honey sugar profile can provide an accurate and reliable tool for detecting indirect honey adulteration by sucrose solution.

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Honey botanical origin classification using hyperspectral imaging and machine learning

Cited by 43 publications

References 163 publications

Nondestructive Phenolic Compounds Measurement and Origin Discrimination of Peated Barley Malt Using Near-Infrared Hyperspectral Imagery and Machine Learning

Nondestructive Phenolic Compounds Measurement and Origin Discrimination of Peated Barley Malt Using Near-Infrared Hyperspectral Imagery and Machine Learning

Fraud in Animal Origin Food Products: Advances in Emerging Spectroscopic Detection Methods over the Past Five Years

Classification and Prediction of Bee Honey Indirect Adulteration Using Physiochemical Properties Coupled with K-Means Clustering and Simulated Annealing-Artificial Neural Networks (SA-ANNs)

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