Vis-NIR hyperspectral imaging along with Gaussian process regression to monitor quality attributes of apple slices during drying

Arefi, Arman; Sturm, Barbara; Gersdorff, Gardis von; Nasirahmadi, Abozar; Hensel, Oliver

doi:10.1016/j.lwt.2021.112297

Cited by 22 publications

(15 citation statements)

References 39 publications

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“…This could be as a result of unevenness in TPC assessed at each point of drying leading to an underfitting problem. A similar finding was reported by [34].…”

Section: Chemical Attributessupporting

confidence: 91%

“…However, new PLSR models based on reduced spectra offer the possibility to collect smaller datasets. This would simplify the data acquisition process, decrease data acquisition times and reduce the computational demand as compared to utilising full spectrum PLSR models [34,124,135]. The simplification of the data acquisition and computation process provides the possibility to implement real-time data acquisition to control the drying process.…”

Section: Discussionmentioning

confidence: 99%

“…As opposed to conventional Vis-NIR spectroscopy, HSI provides spatial information of significance to particular product properties over a wide spectrum [24]. In the food industry, HSI has been applied to predict various quality attributes including fatty acid profiles in almonds and whole dried cocoa beans [25,26], soluble solids content and firmness in plums, apricots, kiwifruits and grapes [27,28], fibre content in pasta [29], microstructural changes in dry-cured pork [30], sugar content in potatoes [31], micronutrients in wheat [32], colour and acid content in hops [33], moisture content, colour, vitamin C, soluble solids content, firmness, shrinkage and rehydration ratio in apples [5,34,35], etc. In regard to cocoyam, NIR spectroscopy in the spectral range 350-2500 nm was applied to assess the content of starch, total sugars, cellulose, proteins, and minerals in 315 accessions of cocoyam for varietal selection and breeding [36].…”

Section: Introductionmentioning

confidence: 99%

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Vis-NIR Hyperspectral Imaging for Online Quality Evaluation during Food Processing: A Case Study of Hot Air Drying of Purple-Speckled Cocoyam (Colocasia esculenta (L.) Schott)

et al. 2021

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In this study, hyperspectral imaging (HSI) and chemometrics were implemented to develop prediction models for moisture, colour, chemical and structural attributes of purple-speckled cocoyam slices subjected to hot-air drying. Since HSI systems are costly and computationally demanding, the selection of a narrow band of wavelengths can enable the utilisation of simpler multispectral systems. In this study, 19 optimal wavelengths in the spectral range 400–1700 nm were selected using PLS-BETA and PLS-VIP feature selection methods. Prediction models for the studied quality attributes were developed from the 19 wavelengths. Excellent prediction performance (RMSEP < 2.0, r2P > 0.90, RPDP > 3.5) was obtained for MC, RR, VS and aw. Good prediction performance (RMSEP < 8.0, r2P = 0.70–0.90, RPDP > 2.0) was obtained for PC, BI, CIELAB b*, chroma, TFC, TAA and hue angle. Additionally, PPA and WI were also predicted successfully. An assessment of the agreement between predictions from the non-invasive hyperspectral imaging technique and experimental results from the routine laboratory methods established the potential of the HSI technique to replace or be used interchangeably with laboratory measurements. Additionally, a comparison of full-spectrum model results and the reduced models demonstrated the potential replacement of HSI with simpler imaging systems.

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“…This could be as a result of unevenness in TPC assessed at each point of drying leading to an underfitting problem. A similar finding was reported by [34].…”

Section: Chemical Attributessupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Vis-NIR Hyperspectral Imaging for Online Quality Evaluation during Food Processing: A Case Study of Hot Air Drying of Purple-Speckled Cocoyam (Colocasia esculenta (L.) Schott)

et al. 2021

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“…In simple words, the wavelength region allows capturing the fundamental peaks related to the target attribute such as moisture, dryness, or specific compositional element that could indirectly be linked to bacterial spoilage. Moisture could be a direct measurement of food quality (Arefi et al., 2021) or could be used as an indicator of other processes such as drying (Crichton et al., 2018). In another example, for detecting total viable count (TVC) in pork samples, the required wavelength range covered visible (400–700 nm), and/or NIR with peaks related to fat (around 900 and 1200 nm) and/or other nutrients such as glycogen or protein.…”

Section: Hyperspectral Imaging: the State Of The Artmentioning

confidence: 99%

Hyperspectral imaging and machine learning in food microbiology: Developments and challenges in detection of bacterial, fungal, and viral contaminants

Soni

Dixit

Reis

et al. 2022

Comp Rev Food Sci Food Safe

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Hyperspectral imaging (HSI) is a robust and nondestructive method that can detect foreign particles such as microbial, chemical, and physical contamination in food. This review summarizes the work done in the last two decades in this field with a highlight on challenges, risks, and research gaps. Considering the challenges of using HSI on complex matrices like food (e.g., the confounding and masking effects of background signals), application of machine learning and modeling approaches that have been successful in achieving better accuracy as well as increasing the detection limit have also been discussed here. Foodborne microbial contaminants such as bacteria, fungi, viruses, yeast, and protozoa are of interest and concern to food manufacturers due to the potential risk of either food poisoning or food spoilage. Detection of these contaminants using fast and efficient methods would not only prevent outbreaks and recalls but will also increase consumer acceptance and demand for shelf-stable food products. The conventional culture-based methods for microbial detection are time and labor-intensive, whereas hyperspectral imaging (HSI) is robust, nondestructive with minimum sample preparation, and has gained significant attention due to its rapid approach to detection of microbial contaminants. This review is a comprehensive summary of the detection of bacterial, viral, and fungal contaminants in food with detailed emphasis on the specific modeling and datamining approaches used to overcome the specific challenges associated with background and data complexity.

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“…Fortunately, with the advantage of low testing cost, high efficiency, good reproducibility of test results, and non-destructive testing, NIR spectroscopy, between wavelength region range of 780–2,526 nm, has been applied popularly in the analysis of different fruit or vegetable samples (Beghi et al, 2017 ; Arendse et al, 2018 ), such as apple (Xia et al, 2020 ; Arefi et al, 2021 ; Ma et al, 2021 ; Li et al, 2022 ), tomato (Huang et al, 2021 ; Zhang et al, 2021 ), persimmon (Wei et al, 2020 ), pear (Cruz et al, 2021 ), and banana (Cruz et al, 2021 ). Xia et al ( 2020 ) studied the effect of sample diameter differences on the online prediction of SSC of “Fuji” apples with the methods of visible and near-infrared spectroscopy and partial least square regression.…”

Section: Introductionmentioning

confidence: 99%

Soluble Solids Content Binary Classification of Miyagawa Satsuma in Chongming Island Based on Near Infrared Spectroscopy

et al. 2022

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Citrus is one of the most important fruits in China. Miyagawa Satsuma, one kind of citrus, is a nutritious agricultural product with regional characteristics of Chongming Island. Near-infrared Spectroscopy (NIR) is a proper method for studying the quality of fruits, because it is low-cost, efficient, non-destructive, and repeatable. Therefore, the NIR technique is used to detect citrus's soluble solid content (SSC) in this study. After obtaining the original spectral data, the first 70% of them are divided into the training set and 30% into the test set. Then, the Random Frog algorithm is chosen to select characteristic wavelengths, which reduces the dimension of the data and the complexity of the model, and accordingly makes the generalization of the classification model better. After comparing the performance of various classifiers (AdaBoost, KNN, LS-SVM, and Bayes) under different characteristic wavelength numbers, the AdaBoost classifier outperforms using 275 characteristic wavelengths for modeling eventually. The accuracy, precision, recall, and F1-score are 78.3%, 80.5%, 78.3%, and 0.780, respectively and the ROC (Receiver Operating Characteristic Curve, ROC curve) is close to the upper left corner, suggesting that the classification model is acceptable. The results demonstrate that it is feasible to use the NIR technique to estimate whether the citrus is sweet or not. Furthermore, it is beneficial for us to apply the obtained models for identifying the quality of citrus correctly. For fruit traders, the model helps them to determine the growth cycle of citrus more scientifically, improve the level of citrus cultivation and management and the final fruit quality, and thus increase the economic income of fruit traders.

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Vis-NIR hyperspectral imaging along with Gaussian process regression to monitor quality attributes of apple slices during drying

Cited by 22 publications

References 39 publications

Vis-NIR Hyperspectral Imaging for Online Quality Evaluation during Food Processing: A Case Study of Hot Air Drying of Purple-Speckled Cocoyam (Colocasia esculenta (L.) Schott)

Vis-NIR Hyperspectral Imaging for Online Quality Evaluation during Food Processing: A Case Study of Hot Air Drying of Purple-Speckled Cocoyam (Colocasia esculenta (L.) Schott)

Hyperspectral imaging and machine learning in food microbiology: Developments and challenges in detection of bacterial, fungal, and viral contaminants

Soluble Solids Content Binary Classification of Miyagawa Satsuma in Chongming Island Based on Near Infrared Spectroscopy

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