Rancidity and moisture estimation in shelled almond kernels using NIR hyperspectral imaging and chemometric analysis

Panda, Brajesh Kumar; Mishra, Gayatri; Ramirez, Wilmer Ariza; Jung, Hye-Won; Singh, Chandra B.; Lee, Sang‐Heon; Lee, Ivan

doi:10.1016/j.jfoodeng.2021.110889

Cited by 35 publications

(12 citation statements)

References 42 publications

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“…The image obtained is an HSI image showing the chemical composition and physical properties of the food samples [ 27 ]. HSI can operate in the wavelength range of 780–2500 nm, which depends on the chemical nature of the food samples [ 28 ].…”

Section: Hsi Technologymentioning

confidence: 99%

“…Chen et al (2022) proposed a successive projection algorithm to select the optimal wavelength based on the complete spectral range to predict the chemical composition of the product [ 8 ]. Lin et al (2022) employed RC, CARS [ 28 ] and GA algorithms to decrease the number of wavelengths and derive specific wavelengths of the main spectrum [ 3 ]. To compare the efficiency of the derived wavelengths, they employed these wavelengths as input data to develop the PLSR, SVM and MLR models.…”

Section: Processes On the Spectramentioning

confidence: 99%

“…The development of models using the selected wavelengths was performed to simplify the prediction process and improve the calculation speed. One of them was the MLR models [ 28 ].…”

Section: Processes On the Spectramentioning

confidence: 99%

“…An HSI technology (Specim FX17, Spectral Imaging Ltd., Oulu, Finland) in the spectral range of 900–1700 nm was adopted for rapid and non-destructive determination of MC, free fatty acids (FFA) and peroxide value (PV) of the shelled almonds [ 28 ]. All analyses were performed in MATLAB 2019b software.…”

Section: Comparison Of Various Techniques For Prediction Of Moisture ...mentioning

confidence: 99%

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The Application of Hyperspectral Imaging Technologies for the Prediction and Measurement of the Moisture Content of Various Agricultural Crops during the Drying Process

2023

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Drying is one of the common procedures in the food processing steps. The moisture content (MC) is also of crucial significance in the evaluation of the drying technique and quality of the final product. However, conventional MC evaluation methods suffer from several drawbacks, such as long processing time, destruction of the sample and the inability to determine the moisture of single grain samples. In this regard, the technology and knowledge of hyperspectral imaging (HSI) were addressed first. Then, the reports on the use of this technology as a rapid, non-destructive, and precise method were explored for the prediction and detection of the MC of crops during their drying process. After spectrometry, researchers have employed various pre-processing and merging data techniques to decrease and eliminate spectral noise. Then, diverse methods such as linear and multiple regressions and machine learning were used to model and predict the MC. Finally, the best wavelength capable of precise estimation of the MC was reported. Investigation of the previous studies revealed that HSI technology could be employed as a valuable technique to precisely control the drying process. Smart dryers are expected to be commercialised and industrialised soon by the development of portable systems capable of an online MC measurement.

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Section: Hsi Technologymentioning

confidence: 99%

Section: Processes On the Spectramentioning

confidence: 99%

“…The development of models using the selected wavelengths was performed to simplify the prediction process and improve the calculation speed. One of them was the MLR models [ 28 ].…”

Section: Processes On the Spectramentioning

confidence: 99%

Section: Comparison Of Various Techniques For Prediction Of Moisture ...mentioning

confidence: 99%

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The Application of Hyperspectral Imaging Technologies for the Prediction and Measurement of the Moisture Content of Various Agricultural Crops during the Drying Process

2023

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“…In this sense, Torres et al [8] detected the presence of bitter almonds in commercial batches of sweet almonds using two portable NIRS devices, and Vega-Castellote et al [9] used NIRS technology as a non-targeted control method to detect non-compliant or adulterated sweet almond batches. However, although hyperspectral imaging (HSI) has been proved to be suitable for quality estimation and fraud detection in the almond sector [10][11][12][13][14], there are no published articles related to the use of this technique for the detection of bitter almonds in commercial sweet almond batches. HSI presents a fundamental advantage for this purpose as it acquires both spatial and spectral information of each sample, combining them to provide a unique fingerprint for each pixel of the image [15], which is a key feature in dealing with the food heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Imaging for the Detection of Bitter Almonds in Sweet Almond Batches

et al. 2022

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A common fraud in the sweet almond industry is the presence of bitter almonds in commercial batches. The presence of bitter almonds not only causes unpleasant flavours but also problems in the commercialisation and toxicity for consumers. Hyperspectral Imaging (HSI) has been proved to be suitable for the rapid and non-destructive quality evaluation in foods as it integrates the spectral and spatial dimensions. Thus, we aimed to study the feasibility of using an HSI system to identify single bitter almond kernels in commercial sweet almond batches. For this purpose, sweet and bitter almond batches, as well as different mixtures, were analysed in bulk using an HSI system which works in the spectral range 946.6–1648.0 nm. Qualitative models were developed using Partial Least Squares-Discriminant Analysis (PLS-DA) to differentiate between sweet and bitter almonds, obtaining a classification success of over the 99%. Furthermore, data reduction, as a function of the most relevant wavelengths (VIP scores), was applied to evaluate its performance. Then, the pixel-by-pixel validation of the mixtures was carried out, identifying correctly between 61–85% of the adulterations, depending on the group of mixtures and the cultivar analysed. The results confirm that HSI, without VIP scores data reduction, can be considered a promising approach for classifying the bitterness of almonds analysed in bulk, enabling identifying individual bitter almonds inside sweet almond batches. However, a more complex mathematical analysis is necessary before its implementation in the processing lines.

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Application of Near-Infrared (NIR) Hyperspectral Imaging System for Protein Content Prediction in Chickpea Flour

Saha,

Senthilkumar,

Singh

et al. 2023

Communications in Computer and Information Science

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Rancidity and moisture estimation in shelled almond kernels using NIR hyperspectral imaging and chemometric analysis

Cited by 35 publications

References 42 publications

The Application of Hyperspectral Imaging Technologies for the Prediction and Measurement of the Moisture Content of Various Agricultural Crops during the Drying Process

The Application of Hyperspectral Imaging Technologies for the Prediction and Measurement of the Moisture Content of Various Agricultural Crops during the Drying Process

Hyperspectral Imaging for the Detection of Bitter Almonds in Sweet Almond Batches

Application of Near-Infrared (NIR) Hyperspectral Imaging System for Protein Content Prediction in Chickpea Flour

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