Prediction of Microbial Spoilage and Shelf-Life of Bakery Products Through Hyperspectral Imaging

Saleem, Zainab; Khan, Muhammad Hussain; Sohaib, Ahmed; Ayaz, Hamail; Mazzara, Manuel

doi:10.1109/access.2020.3026925

Cited by 30 publications

(18 citation statements)

References 47 publications

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“…Higher-order derivatives cause a reduction in signal-to-noise ratio (SNR). Savitzky Golay filter is one of the mathematical derivative methods that are better than others due to its soothing property [ 42 , 43 , 44 , 45 ]. The Savitzky Golay filter is based on polynomial fitting on data points depending upon window size.…”

Section: Methodsmentioning

confidence: 99%

Hyperspectral Imaging for Bloodstain Identification

Zulfiqar

Ahmad

Sohaib

et al. 2021

Sensors

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Blood is key evidence to reconstruct crime scenes in forensic sciences. Blood identification can help to confirm a suspect, and for that reason, several chemical methods are used to reconstruct the crime scene however, these methods can affect subsequent DNA analysis. Therefore, this study presents a non-destructive method for bloodstain identification using Hyperspectral Imaging (HSI, 397–1000 nm range). The proposed method is based on the visualization of heme-components bands in the 500–700 nm spectral range. For experimental and validation purposes, a total of 225 blood (different donors) and non-blood (protein-based ketchup, rust acrylic paint, red acrylic paint, brown acrylic paint, red nail polish, rust nail polish, fake blood, and red ink) samples (HSI cubes, each cube is of size 1000 × 512 × 224, in which 1000 × 512 are the spatial dimensions and 224 spectral bands) were deposited on three substrates (white cotton fabric, white tile, and PVC wall sheet). The samples are imaged for up to three days to include aging. Savitzky Golay filtering has been used to highlight the subtle bands of all samples, particularly the aged ones. Based on the derivative spectrum, important spectral bands were selected to train five different classifiers (SVM, ANN, KNN, Random Forest, and Decision Tree). The comparative analysis reveals that the proposed method outperformed several state-of-the-art methods.

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Section: Methodsmentioning

confidence: 99%

Hyperspectral Imaging for Bloodstain Identification

Zulfiqar

Ahmad

Sohaib

et al. 2021

Sensors

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“…PCA is widely used in food chemistry studies [37] and specifically for analysis of food spoilage. For example, in 2020 Saleem et al [38] presented a new method for predicting microbial spoilage and detecting its location in bakery goods using HSI. HSI cameras monitored baked goods over a period of time as they were allowed to spoil.…”

Section: Feature Extractionmentioning

confidence: 99%

“…A reliable and robust machine learning classifier, the SVM has been used in many hyperspectral imaging food analysis applications. A few examples since 2020 include the detection of spoilage in visible-NIR imagery of baked goods [38], detection of bacterial foodborne pathogens in visible-NIR imagery [57], and detection of fish fillet substitution and mislabeling through accurate classification of fillet species from imagery collected from visible-NIR, fluorescence with UV excitation, SWIR, and Raman spectral bands [58].…”

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

Advanced Optical Technologies in Food Quality and Waste Management

Chauvin¹,

Duran²,

Ng³

et al. 2021

Innovation in the Food Sector Through the Valorization of Food and Agro-Food by-Products

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Food waste is a global problem caused in large part by premature food spoilage. Seafood is especially prone to food waste because it spoils easily. Of the annual 4.7 billion pounds of seafood destined for U.S. markets between 2009 and 2013, 40 to 47 percent ended up as waste. This problem is due in large part to a lack of available technologies to enable rapid, accurate, and reliable valorization of food products from boat or farm to table. Fortunately, recent advancements in spectral sensing technologies and spectroscopic analyses show promise for addressing this problem. Not only could these advancements help to solve hunger issues in impoverished regions of the globe, but they could also benefit the average consumer by enabling intelligent pricing of food products based on projected shelf life. Additional technologies that enforce trust and compliance (e.g., blockchain) could further serve to prevent food fraud by maintaining records of spoilage conditions and other quality validation at all points along the food supply chain and provide improved transparency as regards contract performance and attribution of liability. In this chapter we discuss technologies that have enabled the development of hand-held spectroscopic devices for detecting food spoilage. We also discuss some of the analytical methods used to classify and quantify spoilage based on spectral measurements.

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“…In the literature, hyperspectral imagery has been used to extract information about bruises in peaches [23], and strawberries [24] with significant accuracies. Moreover, HISs have been used to analyze skim and nonfat milk powder [25] and to predict microbial spoilage of bakery products [26] which demonstrate the applicability of HISs at various levels in the food chain. Although HISs offer superior spectral features to MISs, it demands complex systems to control and standardize the image acquisition process.…”

Section: Introductionmentioning

confidence: 97%

Transmittance Multispectral Imaging for Reheated Coconut Oil Differentiation

et al. 2022

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Oil reheating has a significant impact on global health due to its extensive consumption, especially in South Asia, and severe health risks. Nevertheless, food image analysis using multispectral imaging systems(MISs) has not been applied to oil reheating analysis despite their vast application in rapid food quality screening. To that end, the paper discusses the application of a low-cost MSI to estimate the 'reheat cycle count classes' (number of times an oil sample is recursively heated) and identify 'critical classes' at which substantial changes in the oil sample have materialized. Firstly, the reheat cycle count class is estimated with Bhattacharyya distance between the reheated and a pure oil sample as the input. The classification was performed using a support vector machine classifier that resulted in an accuracy of 83.34 % for reheat cycle count identification. Subsequently, an unsupervised clustering procedure was introduced using a modified spectral clustering (SC) algorithm to distinguish critical classes under reheating. In addition, laboratory experiments were performed to ascertain the ramifications of the reheating process with a chemical analysis. The chemical analysis of the coconut oil samples used in the experiment coincided with the chemical analysis results and was statistically significant (p < 0.05). Accordingly, the proposed work closes the gap for using multispectral imaging for oil reheating and proposes a novel algorithm for unsupervised detection of critical property changes in the oil. Hence, the proposed research work is significant in its practical implications, contribution to food image analysis, and unsupervised classification mechanisms.

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Prediction of Microbial Spoilage and Shelf-Life of Bakery Products Through Hyperspectral Imaging

Cited by 30 publications

References 47 publications

Hyperspectral Imaging for Bloodstain Identification

Hyperspectral Imaging for Bloodstain Identification

Advanced Optical Technologies in Food Quality and Waste Management

Transmittance Multispectral Imaging for Reheated Coconut Oil Differentiation

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