Studies on banana fruit quality and maturity stages using hyperspectral imaging
TL;DR: Banana quality and maturity stages were studied at three different temperatures, viz., 20, 25, and 30 °C by using hyperspectral imaging technique in the visible and near infrared (400-1000) regions.
About: This article is published in Journal of Food Engineering.The article was published on 2012-01-01. It has received 263 citations till now. The article focuses on the topics: Coefficient of determination.
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TL;DR: It is evident that hyperspectral imaging can automate a variety of routine inspection tasks and is anticipated that real-time food monitoring systems with this technique can be expected to meet the requirements of the modern industrial control and sorting systems in the near future.
Abstract: In recent years, hyperspectral imaging has gained a wide recognition as a non-destructive and fast quality and safety analysis and assessment method for a wide range of food products. As the second part of this review, applications in quality and safety determination for food products are presented to illustrate the capability of this technique in the food industry for classification and grading, defect and disease detection, distribution visualization of chemical attributes, and evaluations of overall quality of meat, fish, fruits, vegetables, and other food products. The state of the art of hyperspectral imaging for each of the categories was summarized in the aspects of the investigated quality and safety attributes, the used systems (wavelength range, acquisition mode), the data analysis methods (feature extraction, multivariate calibration, variables selection), and the performance (correlation, error, visualization). With its success in different applications of food quality and safety analysis and assessment, it is evident that hyperspectral imaging can automate a variety of routine inspection tasks. Industrial relevance It is anticipated that real-time food monitoring systems with this technique can be expected to meet the requirements of the modern industrial control and sorting systems in the near future.
461 citations
TL;DR: The different technologies available to acquire the images and their use for the non-destructive inspection of the internal and external features of these products are explained, with details of the statistical techniques most commonly used for this task.
Abstract: Hyperspectral imaging systems are starting to be used as a scientific tool for food quality assessment. A typical hyperspectral image is composed of a set of a relatively wide range of monochromatic images corresponding to continuous wavelengths that normally contain redundant information or may exhibit a high degree of correlation. In addition, computation of the classifiers used to deal with the data obtained from the images can become excessively complex and time-consuming for such high-dimensional datasets, and this makes it difficult to incorporate such systems into an industry that demands standard protocols or high-speed processes. Therefore, recent works have focused on the development of new systems based on this technology that are capable of analysing quality features that cannot be inspected using visible imaging. Many of those studies have also centred on finding new statistical techniques to reduce the hyperspectral images to multispectral ones, which are easier to implement in automatic, non-destructive systems. This article reviews recent works that use hyperspectral imaging for the inspection of fruit and vegetables. It explains the different technologies available to acquire the images and their use for the non-destructive inspection of the internal and external features of these products. Particular attention is paid to the works aimed at reducing the dimensionality of the images, with details of the statistical techniques most commonly used for this task.
444 citations
Cites background from "Studies on banana fruit quality and..."
...Finally, banana fruit quality and maturity stages were studied by Rajkumar et al. (2012) at three different temperatures...
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TL;DR: This paper presents hyperspectral and multispectral imaging technologies in the area of food safety and quality evaluation, with an introduction, demonstration, and summarization of current spectral imaging techniques available to the food industry for practical commercial use.
289 citations
TL;DR: This review provides an overview of wavelength selection methods in food-related areas and offers a thoughtful perspective on future potentials and challenges in the development of HSI systems.
Abstract: During the past decade, hyperspectral imaging (HSI) has been rapidly developing and widely applied in the food industry by virtue of the use of chemometric techniques in which wavelength selection methods play an important role. This paper is a review of such variable selection methods and their limitations, describing the basic taxonomy of the methods and their respective advantages and disadvantages. Special attention is paid to recent developments in wavelength selection techniques for HSI in the field of food quality and safety evaluations. Typical and commonly used methods in HSI, such as partial least squares regression, stepwise regression and spectrum analysis, are described in detail. Some sophisticated methods, such as successive projections algorithm, uninformative variable elimination, simulated annealing, artificial neural network and genetic algorithm methods, are also discussed. Finally, new methods not currently used but that could have substantial impact on the field are presented. In short, this review provides an overview of wavelength selection methods in food-related areas and offers a thoughtful perspective on future potentials and challenges in the development of HSI systems.
284 citations
Cites methods from "Studies on banana fruit quality and..."
...These data indicate that HSI can be used as a non-destructive method for evaluating banana fruit ripening/maturity stages (Rajkumar et al. 2012)....
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...…of studies have focused on selecting feature wavelengths by PLSR for muscle foods, some attempts have also been made to use this method for estimating quality and safety parameters of agricultural products (ElMasry et al. 2008; Bhuvaneswari et al. 2011; Rajkumar et al. 2012; Zhang and He 2013)....
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...…1,345 3 Feng et al. (2013) Bruises of apples 750, 820, 960 3 ElMasry et al. (2008) Banana fruit quality 440, 525, 633, 672, 709, 760, 925, 984 8 Rajkumar et al. (2012) Detect insect in semolina 1,070, 1,200, 1,220, 1,290, 1,330, 1,380, 1,410, 1,520, 1,540, 1,570 10 Bhuvaneswari et al.…...
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...While the majority of studies have focused on selecting feature wavelengths by PLSR for muscle foods, some attempts have also been made to use this method for estimating quality and safety parameters of agricultural products (ElMasry et al. 2008; Bhuvaneswari et al. 2011; Rajkumar et al. 2012; Zhang and He 2013)....
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TL;DR: In combination with appropriate spectra pre-processing and chemometric technique, hyperspectral imaging stands out as an advanced quality evaluation system for food and agricultural products.
Abstract: Hyperspectral imaging is built with the aggregation of imaging, spectroscopy and radiometric techniques. This technique observes the sample behaviour when it is exposed to light and interprets the properties of the biological samples. As hyperspectral imaging helps in interpreting the sample at the molecular level, it can distinguish very minute changes in the sample composition from its scatter properties. Hyperspectral data collection depends on several parameters such as electromagnetic spectrum wavelength range, imaging mode and imaging system. Spectral data acquired using a hyperspectral imaging system contain variations due to external factors and imaging components. Moreover, food samples are complex matrices with conditions of surface and internal heterogeneities, which may lead to variations in acquired data. Hence, before extracting information, these variations and noises must be reduced from the data using reference-dependent or reference-independent spectral pre-processing techniques. Using of the entire hyperspectral data for information extraction is tedious and time-consuming. In order to overcome this, exploratory data analysis techniques are used to select crucial wavelengths from the excessive hyperspectral data. Using appropriate chemometric techniques (supervised or unsupervised learning techniques) on this pre-processed hyperspectral data, qualitative or quantitative information from sample can be obtained. Qualitative information for analysing of the chemical composition, detecting of the defects and determining the purity of the food product can be extracted using discriminant analysis techniques. Quantitative information including variation in chemical constituents and contamination levels in food and agricultural sample can be extracted using categorical regression techniques. In combination with appropriate spectra pre-processing and chemometric technique, hyperspectral imaging stands out as an advanced quality evaluation system for food and agricultural products.
167 citations
References
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15 Nov 2001
1,827 citations
TL;DR: In this paper, a multivariate calibration technique was used for the determination of moisture content (MC), total soluble solids (TSS), and acidity (expressed as pH) in strawberry images.
413 citations
TL;DR: In this paper, a high spatial resolution (0.5-1.0 mm) hyperspectral imaging system is presented as a tool for selecting better multispectral methods to detect defective and contaminated foods and agricultural products.
326 citations
TL;DR: The classification results indicated that the bruised apples were successfully distinguished from the sound apples and the accuracy of the system is superior to detect bruise after 1 h of bruising.
Abstract: The potentials of a hyperspectral imaging system were investigated for early detection of bruises on ‘McIntosh’ apples. A hyperspectral imaging system was developed based on a spectral region between 400 and 1000 nm. Partial least squares method and stepwise discrimination analysis were used for data dimensionality reduction and selecting the effective wavelengths. Three effective wavelengths in the near infrared region (750, 820, 960 nm) were selected to realize multispectral imaging tests. The classification results indicated that the bruised apples were successfully distinguished from the sound apples. The accuracy of the system is superior to detect bruise after 1 h of bruising.
240 citations
TL;DR: In this article, a hyperspectral imaging system was used to simultaneously acquire 153 spectral scattering profiles, generated by a broadband light beam, from ‘Red Haven’ and ‘Coral Star’ peaches between 500 and 1000nm.
227 citations