Laser Light Backscattering Imaging Research Articles

A Review of the Application of the Laser-Light Backscattering Imaging Technique to Agricultural Products

Growing concerns about food safety and waste have increased consumer demand for high-quality agricultural products, particularly at the postharvest stage. This demand has prompted the development of non-destructive methods to assess or inspect the internal quality of fruits and vegetables. The backscattering imaging technique, also known as diffuse reflectance imaging, is considered a highly promising approach. Numerous studies have focused on practical applications, using laser light at selected wavelengths to develop quick multispectral methods. Due to the rapid interaction of photons with biological tissue, together with the highly computational performance of machine vision, backscattering imaging can offer a valuable alternative to traditional methods. Its primary benefits include quick measurements without chemical sample preparation, easy integration with high-throughput automatic quality control, and reduced waste, since this non-destructive technique does not damage samples. This review presents a comprehensive overview of backscattering imaging, including the measurement geometry, data analysis, and design considerations for vision systems. Additionally, it explores this technique’s advantages, challenges, and accuracy, as demonstrated using various case studies.

Implementation of laser-light backscattering imaging for authentication of the geographic origin of Indonesia region citrus

Citrus fruit (Citrus nobilis Lour.) from the Indonesian region is reported to have high economic value due to attractive nutritional, nutraceutical, and sensory attributes. However, authenticating the geographic origins is challenging because of adulteration and similarity in visual appearance. Therefore, this study aimed to develop an effective method based on laser-light backscattering imaging (LLBI) for authentication of the geographic origin of the region citrus. A total of 200 citrus samples were collected from Medan, Malang, Jember, and Banyuwangi regions, which were the four main citrus-producing areas in Indonesia. Approximately three different laser wavelengths, namely 450, 532, and 648 nm were beamed to produce the backscattering image. Furthermore, a combination of the gray-level co-occurrence matrix (GLCM) method and support vector machine (SVM) algorithm was applied to extract texture features and build a classification model, respectively. In this context, three kernel functions, such as linear, radial basis function, and polynomial, were compared in authenticating the geographic origin of citrus. The results showed that the proposed technique achieved 96.667 % accuracy and 3.333 % apparent error for authentication of the geographic origin. The proposed LLBI technique applied a laser wavelength of 450 nm and a polynomial kernel function as the best combination to produce reliable predictive power. This study held valuable implications for advancing sensing technology devices to authenticate geographic origin, specifically citrus fruit.

A novel approach for determining coconut drink adulteration by means of laser light backscattering imaging

AbstractIn this work, the simulated adulteration of coconut drink by dilution with water was investigated using laser-light backscattering (LLB) imaging. The laser vision system consisted of six low power laser modules, emitting 1 mm diameter beams at wavelengths of 532, 635, 780, 808, 850 and 1,064 nm. The backscattering images were acquired by a grey scale camera with 12 bit resolution. Eight parameters were extracted to describe the backscattering profile. The methods of linear discriminant analysis (LDA) and partial least squares (PLS) regression were performed on LLB parameters for classifying and predicting dilution level of adulterated coconut drink samples. Based on the results, LLB signals responded sensitively to adulteration. LDA results showed that adulterated samples were correctly recognized with accuracies between 60 and 100%. PLS models were able to estimate the adulteration level of samples with coefficients of determination of 0.57–0.97 in validation. This result demonstrated the potential of laser-light backscattering imaging as a rapid and non-destructive optical technique for evaluation of coconut drink adulteration.

Monitoring of surface changes of fresh green asparagus during storage using laser light backscattering imaging

Monitoring of surface changes of fresh green asparagus during storage using laser light backscattering imaging

Monitoring of banana’s optical properties by laser light backscattering imaging technique during drying

Banana drying is an important process that used to extend the shelf life and increase the marketability of the dried banana. However, this process can lead to changes in weight loss, firmness, and color, which may influence consumer acceptance. As a result, it is crucial to monitor these changes to maintain the desired quality. Therefore, the aim of this study was to assess the quality of sliced bananas during the drying process by simultaneously monitoring their optical and physical properties using laser light backscatter imaging, near-infrared spectroscopy, and electrical impedance spectroscopy techniques. Banana sliced were prepared with 10 mm thickness and immersed into 4% ascorbic acid solution and water as treated and control samples dried at 50 °C for 6 h. The parameters measured were weight loss, color, firmness, NIR absorbance in the range from 740 to 1700 nm and electrical impedance in the frequency range from 30 kHz to 1 MHz. Absorption, reduced scattering and diffusion coefficients, penetration depth and full width at half maximum (FWHM) were computed on the LLBI signal at wavelengths of 532, 635, 780, 808, 850 and 1064 nm. The results showed that both the drying time and the ascorbic acid treatment and their interaction affected the measured values. The strong NIR absorption spectrum changes observed at wavelengths of 1064 and 1416 nm. The least squares partial regression model (PLSR) was performed with high accuracy for weight loss (%) and relaxation time (ms) at a coefficient of determination (R2) of 0.940 and 0.945 with a mean square error (RMSE) of 3.748 and 0.001, respectively. The electrical impedance spectral changes were found in the frequency range from 60 Hz to 1 MHz. The most sensitive laser wave lengths to evaluate optical properties were 532, 635, 780 and 1064 nm. Therefore, laser backscatter imaging together with NIR spectroscopy and impedance spectroscopy is a promising technique to assess the quality of sliced bananas during the drying process.Graphical abstract

Physicochemical Characterization of Texture-Modified Pumpkin by Vacuum Enzyme Impregnation: Textural, Chemical, and Image Analysis

Texture-modified pumpkin was developed by using vacuum enzyme impregnation to soften texture to tolerable limits for the elderly population with swallowing and chewing difficulties. The impregnation process and macrostructural and microstructural enzyme action were explored by the laser light backscattering imaging technique and a microscopic study by digital image analysis. Texture was analyzed by a compression assay. The effect of enzyme treatment on antioxidant capacity and sugar content was evaluated and compared to the traditional cooking effect. Image analysis data demonstrated the effectiveness of the impregnation process and enzyme action on plant cell walls. Enzyme-treated samples at the end of the process had lower stiffness values with no fracture point, significantly greater antioxidant capacity and significantly lower total and reducing sugars contents than traditionally cooked pumpkins. The results herein obtained demonstrate the capability of using vacuum impregnation treatment with enzymes to soften pumpkins and their positive effects on antioxidant capacity and sugar content to develop safe and sensory-accepted texture-modified products for specific elderly populations.

Laser beam technology interventions in processing, packaging, and quality evaluation of foods

Laser emits light using an optical amplification process that is based on the stimulated emission of electromagnetic radiation. Thermal and non-thermal approaches are used in laser material processing. Some of the characteristics of lasers that make them beneficial for food processing are their ability to produce consistent heating, perfect control over the energy supply, the accuracy of spatial placement of the energy, and precise localized heating. Laser is a novel food processing method that can aid with precision cooking. In conjunction with the food layered manufacturing method, the laser cooking method can be used to print three-dimensional (3D) food formulations. In the food packaging sector, laser welding technology has been applied in the film joining process, such as in the flow pack packaging process or in hermetically sealed containers/cans. Soluble solid content and firmness of food products were determined using a laser light backscattering imaging technique. Laser-backscattering technology has also been discussed to model and quantify food attributes, as well as to help in the processing of solid and fluid food matrices. The practice of lasers in the processing of foods, packaging technology, microbial suppression, and food quality evaluation are discussed in this study.

NIR laser-based imaging techniques to monitor quality attributes of apple slices during the drying process: Laser-light backscattering & biospeckle imaging techniques

This paper seeks to investigate the potential use of laser-based imaging techniques in non-invasive quality inspection of apple slices during drying. For this purpose, Laser-light Backscattering (LLBI) and Biospeckle (BSI) Imaging techniques were investigated. Images at 980 nm and 1450 nm were captured. The data was used to develop Gaussian Process Regression (GPR) and Partial Least Square Regression (PLSR) models in order to estimate quality attributes of moisture content, vitamin C, and soluble solids content (SSC). Overall, the predictions accuracies were slightly in favor of GPR models. Moisture content was best estimated with R-squared = 0.92 and RMSE = 7%, and R-squared = 0.93 and RMSE = 6.54% when GPR models were trained on 1450/980 nm data acquired by LLBI or 980 nm data acquired by BSI, respectively. LLBI-based GPR models predicted vitamin C with R-squared ≥ 0.79 and RMSE ≤ 0.83 mg/100 g fresh weight (FW), slightly better than that of BSI (R-squared = 0.80 and RMSE ≤ 1.01 mg/100 g). The most accurate predictions of SSC for LLBI (R-squared = 0.88 and RMSE = 6.39%) and BSI (R-squared = 0.82 and RMSE = 7.99%) were achieved at 980 nm. Furthermore, BSI monitoring system was embedded into an experimental dryer to study the interaction between sample temperature and biospeckle activity. Results showed an increase in biospeckle activity at the beginning of drying followed by a gradual decrease towards the end of drying. The findings of present study indicate the potential use of simple and promising monochrome imaging systems in monitoring of the drying process that can be considered as alternatives to more expensive imaging sensors such as hyperspectral imaging.

Prediction of the quality properties and maturity of apricot by laser light backscattering imaging

The maturity level plays an essential role in the quality and shelf life of apricot. The present research aims to investigate the applicability and accuracy of the non-destructive laser light backscattering imaging method to predict the quality properties of apricot during ripening. The backscattering images of apricots were acquired at 650 nm in six stages of ripening. The images were segmented by two different thresholding techniques, and several space domain features were extracted from the segmented images. Artificial neural network (ANN), partial least squares regression (PLSR), and principle component analysis-artificial neural network (PCA-ANN) models were developed to predict the firmness and total soluble solids (TSS) of apricot using each of the extracted image features and their combination as input for the prediction models. Results revealed a high correlation between the extracted features from the backscattering images and the quality parameters of apricot during ripening. Modeling using ANN recorded better performance than PLSR. The highest coefficient of determination (R2) and the lowest root mean squared error (RMSE) of cross-validation were achieved with ANN as R2CV = 0.974, RMSECV = 3.482 and R2CV = 0.963, RMSECV = 1.146 for firmness and TSS, respectively. The results confirmed that the laser backscattering imaging method was successful in predicting the quality properties of apricot during ripening.

Potential application of laser-based imaging technology in the quality evaluation of agricultural products : A review

Non-destructive quality evaluation of agricultural products particularly during postharvest stage has been a primary concern in recent years. The laser-based imaging technology is one of the most promising non-invasive tools which demonstrate potential ability to replace the conventional methods of quality monitoring that are time-consuming, expensive, laborious, inaccurate and most of all destructive. Hence, in this paper, we briefly reviewed the potential application of laser-light backscattering imaging technique (LLBI) as a non-destructive quality evaluation tool applied in agricultural products. This review mainly reports the current knowledge on the successful implementations of the LLBI in measuring the various quality-related attributes of agricultural products under different postharvest conditions such as in drying, storage, sorting, maturity identification, defect detection, etc. The basic components, uses and considerations of the technique are highlighted in this paper. Moreover, the advantages, drawbacks, measurement methods, data analysis applied as well as the accuracies obtained are briefly summarized.

Laser-light backscattering imaging approach in monitoring and classifying the quality changes of sweet potatoes under different storage conditions

Abstract Optical imaging techniques have gained wide attention for quality detection of agricultural and food products. In this work, the non-destructive ability of the laser-light backscattering imaging technique (LLBI) for monitoring and classifying the quality changes of sweet potatoes under different storage conditions was investigated. Freshly-harvested sweet potato root samples were stored at 5 °C, 15 °C and 30 °C for a period of 21 d with 120 samples in each storage group. Laser diode emitting light at 658 nm wavelength along with the camera system was employed to capture the backscattered light from the subjected samples. The acquired backscattering images were then pre-processed and segmented, and the intended backscattering parameters (BP) were extracted. Quality parameters (QP) such as moisture content (MC), soluble solids content (SSC), texture and color properties (L*, a*, b*) were measured using the conventional methods as standard reference data. Multivariate analysis in terms of partial least squares regression (PLSR), principal component analysis (PCA) and linear discriminant analysis (LDA) was carried out to correlate and classify the sweet potatoes based on the BP. Results showed that storage had a significant effect both in the BP and QP of sweet potatoes as well as in the interaction between the BP and the treatments (day and temperature) applied. Among all the QP, SSC gave the most promising results (R = 0.56-0.66; RMSE = 0.76–1.10) across all the storage conditions. The analysis also revealed that 15 °C was the most suitable storage condition with the favourable PLSR results (R > 0.50) in all the examined parameters. Moreover, variations on the BP of the samples with respect to the different storage conditions were correctly classified with over 90 % and 80 % accuracies using the PCA and LDA, respectively. Thus, the study indicates that the LLBI technique is feasible and can be a useful tool for a non-destructive quality measurement and classification of sweet potatoes under different storage conditions.

Combination of laser-light backscattering imaging and computer vision for rapid determination of oil palm fresh fruit bunches maturity

The classification of oil palm fresh fruit bunches (FFB) in relation to the maturity level is an important aspect to determine the quality and productivity of the fruit. This study evaluated the utilization of combined computer vision and laser-light backscattering imaging in determining the oil content and color changes of oil palm FFB at different maturity levels i.e. unripe, ripe, and overripe. Red-green-blue (RGB) images referring to the computer vision and backscattering images of 90 oil palm FFB samples were acquired with 30 samples per each maturity level. Standard reference methods for oil content and color values (L*, a*, and b*) were determined in relation to the quality attributes of the oil palm FFB samples. Partial least squares (PLS) and principal component analysis (PCA) were used to analyze the quality changes of oil palm FFB based on its maturity levels. Multivariate algorithms such as linear discriminant analysis and quadratic discriminant analysis were applied to evaluate the classification performance based on the combined RGB and backscattering parameters. The combined optical techniques showed a good coefficient of determination (R2) of greater than 0.80 for both oil content and color values. The average classification accuracies were also higher than 85% in classifying oil palm FFB maturity. Hence, this work has demonstrated that combined computer vision and backscattering imaging systems could be useful as a non-destructive device for evaluating the classification of oil palm FFB.

Quality evaluation of sweet potatoes (Ipomoea batatas L.) of different varieties using laser light backscattering imaging technique

Non-destructive quality monitoring for horticultural products by means of optical imaging methods have gained wide interest in recent years. This study sought to investigate the potential use of laser-light backscattering imaging technique (LLBI) for non-invasive quality evaluation of sweet potatoes of different varieties after being harvested and stored at ambient temperature. A total of 360 newly harvested samples from the three varieties of locally-produced sweet potatoes in Malaysia i.e. Keledek Anggun 3, Keledek Jingga, and Keledek Kuning were purchased and stored in an ambient room temperature (28–30 °C) for a period of twenty one (21) days. After each 7-day storage, respective samples were taken out and backscattering images (BSI) were acquired using a charge-coupled device (CCD) camera attached with laser diodes emitting lights at 658 nm and 780 nm wavelengths, respectively. Quality attributes such as moisture content (MC), soluble solid content (SSC), textural properties (hardness, fracturability, adhesiveness), and flesh color properties (L*, a*, b*) as reference quality parameters (QP) were measured right after BSI acquisitions. Multiple Linear Regression (MLR) was applied to correlate and predict the relationship between the extracted backscattering parameters (BP) and the QP. Results revealed that there were significant changes (P < 0.05) in the BP during storage for both wavelengths applied. Among all the QP, the textural properties gave the highest correlation (r> 0.70) with BP, particularly the Keledek Kuning variety. The 658 nm wavelength showed better prediction results than 780 nm with r > 0.85 in measuring the textural properties. Thus, the study demonstrated the feasibility of LLBI as a useful non-destructive technique to evaluate the quality of sweet potatoes and can be further utilized for online optical quality grading.

Application of laser-induced backscattering imaging for predicting and classifying ripening stages of “Berangan” bananas

This study investigates the potential of laser light backscattering imaging (LLBI) for predicting and classifying Musa Acuminata cv. Berangan at different ripening stages. Backscattering images of Berangan bananas at various ripening stages from 2 to 7 were acquired by a charge coupled device (CCD) camera coupled with a laser diode emitting light at a wavelength of 658 nm. The grey level intensity and the size of the backscattering area were extracted from the backscattering images and used as parameters for estimating the quality properties of the bananas. The results showed strong correlation between the LLBI parameters and the colour of the bananas as well as the total soluble solids content (TSS) with a coefficient of determination (R2) of over 0.70. Statistical analysis using Linear Discriminant Analysis (LDA) successfully classified the samples into their ripening stages accordingly with the percentage of correct classification up to 94.2%. Thus, it can be concluded that the LLBI could potentially be used for predicting and classifying the ripening stages of bananas and be further developed for an automated quality control system.

Einsatz der bildgebenden Laserlicht Rückstreuungsanalyse (laser light backscattering imaging) für die zerstörungsfreie Bewertung der Effekte essbarer Beschichtungen (edible coatings) auf die Nacherntehaltbarkeit von europäischen Pflaumen (Prunus domestica L.)

Fruits of European plum (Prunus domestica L.) show high rates of transpiration. Consequently, they are highly perishable fruit. In the present study, the potential of an edible coating (Versasheen®-based with sorbitol as plasticizer) to improve storage life and keeping quality was evaluated on fruit of the plum cultivars ‘Jojo’ and ‘Tophit plus’. Plums were picked at commercial harvest date and stored at 2 ± 0.5 °C (90 ± 2% rh) for up to 28 d plus 2 d at 20 °C to simulate shelf life. Every seventh day, plums were analysed for transpiration, colour changes and laser light backscattering imaging (LLBI) at a wavelength of 785 nm to non-destructively investigate variations in quality properties of plums. Finally, fruit flesh firmness, soluble solids content, and titratable acidity were determined destructively. Results indicated that coating of fruit of both cultivars increased their surface resistance against water vapour transfer resulting in lower transpiration rates and thus lower mass losses compared to controls. Consequently, coating of fruit considerably delayed the decrease in flesh firmness. Moreover, edible coating retarded the increase of FWHM785 in coated plums. In this context, FWHM785 proved as a rapid and non-destructive tool to monitor changes in mechanical properties in heavily coloured plums. Results highlight that edible coatings successfully extend post-harvest life of plums.

The potential of computer vision, optical backscattering parameters and artificial neural network modelling in monitoring the shrinkage of sweet potato (Ipomoea batatas L.) during drying.

Drying is a method used to preserve agricultural crops. During the drying of products with high moisture content, structural changes in shape, volume, area, density and porosity occur. These changes could affect the final quality of dried product and also the effective design of drying equipment. Therefore, this study investigated a novel approach in monitoring and predicting the shrinkage of sweet potato during drying. Drying experiments were conducted at temperatures of 50-70 °C and samples thicknesses of 2-6 mm. The volume and surface area obtained from camera vision, and the perimeter and illuminated area from backscattered optical images were analysed and used to evaluate the shrinkage of sweet potato during drying. The relationship between dimensionless moisture content and shrinkage of sweet potato in terms of volume, surface area, perimeter and illuminated area was found to be linearly correlated. The results also demonstrated that the shrinkage of sweet potato based on computer vision and backscattered optical parameters is affected by the product thickness, drying temperature and drying time. A multilayer perceptron (MLP) artificial neural network with input layer containing three cells, two hidden layers (18 neurons), and five cells for output layer, was used to develop a model that can monitor, control and predict the shrinkage parameters and moisture content of sweet potato slices under different drying conditions. The developed ANN model satisfactorily predicted the shrinkage and dimensionless moisture content of sweet potato with correlation coefficient greater than 0.95. Combined computer vision, laser light backscattering imaging and artificial neural network can be used as a non-destructive, rapid and easily adaptable technique for in-line monitoring, predicting and controlling the shrinkage and moisture changes of food and agricultural crops during drying. © 2017 Society of Chemical Industry.

Comparative study of transform-based image texture analysis for the evaluation of banana quality using an optical backscattering system

In this study, the application of a backscattering imaging system with different approaches of transform-based image texture analysis for the evaluation of banana quality at different ripening stages was investigated with Wavelet, Gabor and Tamura transforms. The attenuated images of the fruits were acquired using Laser Light Backscattering Imaging (LLBI) with laser diodes emitting light at three wavelengths viz 532, 660, and 830 nm. The elasticity, chlorophyll index and soluble solids content (SSC) of each sample were measured as reference parameters by using a texture analyser, a Delta Absorbance (DA) meter, and a refractometer, respectively. The performance of the extracted features from the selected transform-based image texture analysis for analysing the quality parameters of the fruit was evaluated by means of an artificial neural network (ANN) and a support vector machine (SVM). The results indicated that there were significant changes of elasticity, chlorophyll index and SSC as the ripening stages increased. Prediction model analysis showed that the Wavelet transform exhibited the most reliable results for all of the reference parameters followed by Tamura and the Gabor transform. The results also revealed that analysis using an ANN approach recorded better performance than SVM as reflected by higher coefficient of determination (R2) values. Thus, this study indicated that an LLBI system with transform-based image texture analysis coupled with computational intelligence techniques can be used for the evaluation of the quality of bananas.

Determination of the difference on color changes of watermelons by laser light backscattering imaging.

The potential of laser light backscattering imaging was investigated for monitoring color parameters of seeded and seedless watermelons during storage. Two watermelon cultivars were harvested and stored for 3 weeks with seven measuring storage days (0, 4, 8, 12, 15, 18, and 21). The color parameters of watermelons were monitored using the conventional colorimetric methods (L*, a*, b*, C*, H*, and ∆E*) and laser light backscattering imaging system. A laser diode emitting at 658nm and 30 mW power was used as a light source to obtain the backscattering image. The backscattering images were evaluated by the extraction of backscattering parameters based on the mean pixel values. The results showed that a good color prediction was achieved by the seedless watermelon with the R2 are all above 0.900. Thus, the application of the laser light backscattering imaging can be used for evaluating the color parameters of watermelons during the storage period.

Using absorption and reduced scattering coefficients for non-destructive analyses of fruit flesh firmness and soluble solids content in pear (Pyrus communis ‘Conference’)—An update when using diffusion theory

Quality attributes of fruit determine its acceptability by the retailer and consumer. The objective of this work was to investigate the potential of absorption (μa) and reduced scattering (μs’) coefficients of European pear to analyze its fruit flesh firmness and soluble solids content (SSC). The absolute reference values, μa* (cm−1) and μs’* (cm−1), of pear were invasively measured, employing multi-spectral photon density wave (PDW) spectroscopy at preselected wavelengths of 515, 690, and 940nm considering two batches of unripe and overripe fruit. On eight measuring dates during fruit development, μa and μs’ were analyzed non-destructively by means of laser light backscattering imaging (LLBI) at similar wavelengths of 532, 660, and 830nm by means of fitting according to Farrell’s diffusion theory, using fix reference values of either μa* or μs’*. Both, the μa* and the μa as well as μs’* and μs’ showed similar trends. Considering the non-destructively measured data during fruit development, μa at 660nm decreased 91 till 141days after full bloom (dafb) from 1.49cm−1 to 0.74cm−1 due to chlorophyll degradation. At 830nm, μa only slightly decreased from 0.41cm−1 to 0.35cm−1. The μs’ at all wavelengths revealed a decreasing trend as the fruit developed. The difference measured at 532nm was most pronounced decreasing from 24cm−1 to 10cm−1, while at 660nm and 830nm values decreased from 15cm−1 to 13cm−1 and from 10cm−1 to 8cm−1, respectively. When building calibration models with partial least-squares regression analysis on the optical properties for non-destructive analysis of the fruit SSC, μa at 532nm and 830nm resulted in a correlation coefficient of R=0.66, however, showing high measuring uncertainty. The combination of all three wavelengths gave an enhanced, encouraging R=0.89 for firmness analysis using μs’ in the freshly picked fruit.

Prediction of banana quality attributes and ripeness classification using artificial neural network

Laser light backscattering imaging (LLBI) with five laser diodes emitting at wavelengths 532, 660, 785, 830, and 1060 nm were employed for predicting quality attributes of banana fruit. The predicted attributes were chlorophyll, elasticity and soluble solids content (SSC). Classifications were done on six ripening stages from ripening stages 2 to 7. The prediction and classification models were built using an artificial neural network (ANN). The results indicated that measurement at 532 nm gave the highest correlation coefficient with 0.949 for chlorophyll prediction, while correlation coefficients of 0.862, 0.867 were the highest obtained for elastic modulus, SSC at 785 and 830 nm, respectively. 95.5% correct classification accuracy was obtained at 830 nm by use of the ANN classification model. The results showed that LLBI with an ANN can be used for non-destructive estimation of banana quality attributes and the subsequent ripeness classification.