The citrus sector is one of the most dynamic and important agricultural sectors. For the international market, it is of great interest the estimation of crop yield prior to harvest, since this yield estimation at the immature green stage could influence the future market price and allow producers to plan the harvest in advance. The aim of this work was to stablish the first steps to set up a methodology for the selection of the relevant bands to distinguish between green oranges and leaves and to detect external defects, which will allow citrus yield to be estimated on tree. Images were acquired from oranges and leaves from an orchard in Jeju island (Jeju, Republic of Korea), using a hyperspectral reflectance imaging system working in the range 400–1000 nm. Analysis of variance (ANOVA) and principal component analysis (PCA) were used to select the main wavelengths for this purpose; next, a band ratio coupled with a simple thresholding method was applied. The system correctly classified over the 90% of the pixels for both objectives, confirming that it is possible to use just few wavelengths to estimate harvest yield in oranges, although further studies are needed for the application of this system in the field, where other factors must be taken into account, such as sun-light illumination, shadows, etc. Therefore, this research can be considered as a preliminary step for designing a multispectral system capable of being mounted on unmanned aerial vehicles (UAVs) to estimate orange yield and defects.
Commercial Raman systems generally conduct imaging and spectroscopy measurements at subcentimeter scales. Such small spatial ranges cannot be used to inspect food samples with large surface areas (e.g., tomato fruit and beef steak), which is not convenient for food experiments. A line-scan macro-scale Raman system has been developed using a 785 nm line laser to implement high-throughput Raman chemical imaging (RCI) for food safety and quality research. A one-axis positioning table is used to move the samples to accumulate hyperspectral data using a pushbroom method. A dispersive Raman spectrograph is used in the system, which can be configured to backscattering RCI mode for surface inspection and spatially offset Raman spectroscopy (SORS) mode for subsurface inspection. In-house developed LabVIEW software is used to fulfill functions for system control, hardware parameterization, and data transfer. The systems is flexible and versatile for food test, and it has been used to evaluate safety and quality of various food and agricultural products, such as detecting chemical adulterants mixed in food powders, mapping carotenoid content on carrot cross section, imaging whole surface of pork shoulder, and authenticating foods and ingredients through packages.
Economically motivated adulteration and fraud to food powders are emerging food safety risks that threaten the health of the general public. In this study, targeted and non-targeted methods were developed to detect adulterants based on macro-scale Raman chemical imaging technique. Detection of potassium bromate (PB) (a flour improver banned in many countries) mixed in wheat flour was used as a case study to demonstrate the developed methods. A line-scan Raman imaging system with a 785 nm line laser was used to acquire hyperspectral image from the flour-PB mixture. Raman data analysis algorithms were developed to fulfill targeted and non-targeted contaminant detection. The targeted detection was performed using a single-band Raman image method. An image classification algorithm was developed based on single-band image at a Raman peak uniquely selected for the PB. On the other hand, a mixture analysis and spectral matching method was used for the non-targeted detection. The adulterant was identified by comparing resolved spectrum with reference spectra stored in a pre-established Raman library of the flour adulterants. For both methods, chemical images were created to show the PB particles mixed in the flour powder.
Despite the complexity of the factors that lead to loss of seed viability, conventional methods like germination tests, tetrazolium tests are commonly employed to determine it. However, these methods have downsides like being destructive, time consuming and non-representative. Therefore, there is a need to develop a fast, non-destructive and real-time measurement and sorting system of seeds based on viability for industrial purpose. In this study, we seek to utilize HSI and multivariate data analysis techniques to classify viable seeds from non-viable ones and later use it basis to develop an online real-time detection system for sorting these seeds based on viability. For this cause, Data from melon and watermelon seeds were collected using a SWIR HSI system. The performance of the classification models achieved both during calibration and real-time tests were quite impressive and a proof that HSI can be effectively applied to an industrial real-time sorting system.
Although many studies have been conducted to detect melamine in milk powder using near-infrared hyperspectral imaging system, the reproducibility due to moisture content in powder sample and detection limit have not been addressed appropriately. The objective of this study is to develop, based on shortwave infrared (SWIR) hyperspectral imaging, optimal model which is less sensitive to change of moisture content in sample powder. The hyperspectral imaging system consists of a MCT-based camera capable of measuring wavelengths from 1000nm to 2500nm. A halogen-based light source module was used to illuminated samples. The results showed a mixture concentration as low as 50 ppm of melamine in milk could be detected. The detection accuracy using the wavelength region from 1700nm to 2500nm was higher than that of using the wavelength from 1000nm to 1700nm. The MCT-based SWIR hyperspectral imaging system has a good potential for the detection and quantification of adulterants in powder sample.
The objective of this study was to predict the moisture content, soluble solids content, and titratable acidity content in bell peppers during storage, based on hyperspectral imaging (HSI) in the 1000–1500 nm wavelength range. The mean spectra of 148 mature bell peppers were extracted from the hyperspectral images, and multivariate calibration models were built using partial least squares regression to predict MC, SSC, and TA content in bell peppers with different preprocessing techniques. The selected optimum wavelengths were used to create distribution maps for MC, SSC, and TA content of bell peppers. The results revealed that HSI coupled with multivariate analysis can be used successfully to predict the MC, SSC, and TA content in bell peppers.
Low temperature environment affects the growth and yield of watermelon negatively. The conventional visual inspection with human eyes has a limitation for accurate phenotyping of the stress symptom. Spectral imaging technique has been used as a useful phenotyping tool for visualizing physiological responses of plants. In this study, responses of chilling stresses of watermelon leaves were investigated using Vis/NIR hyperspectral imaging (HSI) technique. Sensitive and resistant to chilling tolerance of watermelon plants were exposed to low temperature conditions. HSI of the treated leaves were collected and analyzed with multivariate analysis. The result shows that HSI technique could distinguish between susceptible and resistant plants.
Mandarin orange is a popularly consumed fruit in Asian countries. Over 99% of cultivation area in Korea for mandarin oranges is concentrated in Jeju Island. Despite of this high concentration, detecting infection and estimating fruit yields has been done manually, resulting in loss of money and time. In this study, hyperspectral fluorescence imaging technique was explored to distinguish green mandarin oranges from leaves to estimate fruit density. In addition, early stage detection for disease infection of leaves and fruits were investigated. The fluorescence spectral images showed reliable performance for distinguishing green mandarin oranges from leaves, and detecting disease infection on both leaves and fruits. The result demonstrated that hyperspectral fluorescence imaging might be used for rapid and non-destructive detection of disease infection and yield estimation of mandarin orange in the field.
Total volatile basic nitrogen (TVB-N) content is one of the important factors to measure the quality of meat. However, conventional chemical analysis methods for measuring TVB-N contents are time-consuming and labor-intensive, and are destructive procedures. The objective of this study is to investigate the possibility of fluorescence hyperspectral imaging techniques for determination of total volatile basic nitrogen (TVB-N) in beef meat. High intensity LED lights at 365 nm and 405 nm were used as the excitation for acquiring fluorescence images. Prediction algorithms based on simple band-ratio, partial least square discriminant analysis (PLS-DA) have been developed. This study shows that fluorescence hyperspectral imaging system has a good potential for rapid measurement of TVB-N content in meat.
Fishes are a widely used food material in the world. Recently about 4% of the fishes are infected with Kudoa thyrsites in Asian ocean. Kudoa thyrsites is a parasite that is found within the muscle fibers of fishes. The infected fishes can be a reason of food poisoning, which should be sorted out before distribution and consumption. Although Kudoa thyrsites is visible to the naked eye, it could be easily overlooked due to the micro-scale size and similar color with fish tissue. In addition, the visual inspection is labor intensive works resulting in loss of money and time. In this study, a portable microscopic camera was utilized to obtain images of raw fish slices. The optimized image processing techniques with polarized transmittance images provided reliable performance. The result shows that the portable microscopic imaging method can be used to detect parasites rapidly and non-destructively, which could be an alternative to manual inspections.
Excessive use of benzoyl peroxide (BPO, a bleaching agent) in wheat flour can destroy flour nutrients and cause diseases to consumers. A macro-scale Raman chemical imaging method was developed for direct detection of BPO mixed in the wheat flour. A 785 nm line laser was used in a line-scan Hyperspectral Raman imaging system. Raman images were collected from wheat flour mixed with BPO at eight concentrations (w/w) from 50 to 6,400 ppm. A sample holder (150×100×2 mm3) was used to present a thin layer (2 mm thick) of the powdered sample for image acquisition. A baseline correction method was used to correct the fluctuating fluorescence signals from the wheat flour. To isolate BPO particles from the flour background, a simple thresholding method was applied to the single-band fluorescence-free images at a unique Raman peak wavenumber (i.e., 1001 cm−1) preselected for the BPO detection. Chemical images were created to detect and map the BPO particles. Limit of detection for the BPO was estimated in the order of 50 ppm, which is on the same level with regulatory standards.
To achieve comprehensive online quality and safety inspection of fruits, whole-surface sample presentation and imaging regimes must be considered. Specifically, sample presentation method for round objects is under development to achieve effective whole-surface sample evaluation based on the use of a single hyperspectral line-scan imaging device. In this paper, a whole-surface round-object imaging method using hyperspectral line-scan imaging techniques is presented.
The Cucumber Green Mottle Mosaic Virus (CGMMV) is a globally distributed plant virus. CGMMV-infected plants exhibit severe mosaic symptoms, discoloration, and deformation. Therefore, rapid and early detection of CGMMV infected seeds is very important for preventing disease damage and yield losses. Raman spectroscopy was investigated in this study as a potential tool for rapid, accurate, and nondestructive detection of infected seeds. Raman spectra of healthy and infected seeds were acquired in the 400 cm-1 to 1800 cm-1 wavenumber range and an algorithm based on partial least-squares discriminant analysis was developed to classify infected and healthy seeds. The classification model’s accuracies for calibration and prediction data sets were 100% and 86%, respectively. Results showed that the Raman spectroscopic technique has good potential for nondestructive detection of virus-infected seeds.
Fluorescence imaging technique has been widely used for quality and safety measurements of agro-food materials. Fluorescence emission intensities of target materials are influenced by wavelengths of excitation sources. Hence, selection of a proper excitation wavelength is an important factor in differentiating target materials effectively. In this study, optimal fluorescence excitation wavelength was determined on the basis of fluorescence emission intensity of defect and sound areas of cherry tomatoes. The result showed that fluorescence responses of defect and sound surfaces of cherry tomatoes were most significantly separated with the excitation light wavelength range between 400 and 410 nm. Fluorescence images of defect cherry tomatoes were acquired with the LEDs with the central wavelength of 410 nm as the excitation source to verify the detection efficiency of cherry tomato defects. The resultant fluorescence images showed that the defects were discriminated from sound areas on cherry tomatoes with above 98% accuracy. This study shows that high power LEDs as the excitation source for fluorescence imaging are suitable for defect detection of cherry tomatoes.
Bruise damage on pears is one of the most crucial internal quality factors that needs to be detected in postharvest quality
sorting processes. Development of sensitive detection methods for the defects including fruit bruise is necessary to
ensure accurate quality assessment. Infra-red imaging techniques in the 1000 nm to 1700 nm has good potentials for
identifying and detecting bruises since bruises result in the rupture of internal cell walls due to defects on agricultural
materials. In this study, feasibility of hyperspectral infra-red (1000 - 1700 nm) imaging technique for the detection of
bruise damages underneath the pear skin was investigated. Pear bruises, affecting the quality of fruits underneath the
skin, are not easily discernable by using conventional imaging technique in the visible wavelength ranges. Simple image
combination methods as well as multivariate image analyses were explored to develop optimal image analysis algorithm
to detect bruise damages of pear. Results demonstrated good potential of the infra-red imaging techniques for detection
of bruises damages on pears.
Many researchers have been tried to find a rapid pungency measuring method for the capsaicinoids, the main component of spicy to
overcome the disadvantages of the conventional HPLC measurement which is labor-intensive, time-consuming, and expensive. In
this research, an on-line based pungency measuring system for red-pepper powder was developed using a UV/Visible/Near-Infrared
spectrometer with the wavelength range of 400 ~ 1050 nm. The system was constructed with a charge-couple device(CCD)
spectrometer, a reference measuring unit, and a sample transfer unit. Predetermined non-spicy red-pepper powder were
mixed with spicy one (var. Chungyang) to produce samples with a wide range of spicy levels. Total 33 different samples with
11 spicy levels and three particle size(below 0.425 mm, 0.425 ~ 0.71 mm, 0.71 ~ 1.4 mm) were prepared for
measurements. The Partial Least Square Regression Model (PLSR model) was developed to predict the capsaicinoids content with
the obtained spectra using the developed pungency measuring system and compared with the results measured by HPLC. The best
result of PLSR model (R2 = 0.979, SEP = ± 6.56 mg%) was achieved for the spectra of red-pepper powders of the
particle size below 1.4 mm with a pretreatment of smoothing with a 6.5 nm wavelength gap. The results show the
potential of NIRS technique for non-destructive and on-line measurement of capsaicinoids content in red-pepper powder.
Cuticle cracks on tomatoes are potential sites of pathogenic infection that may cause deleterious consequences both to
consumer health and to fresh and fresh-cut produce markets. The feasibility of hyperspectral near-infrared imaging
technique in the spectral range of 1000 nm to 1700 nm was investigated for detecting defects on tomatoes. Spectral
information obtained from the regions of interest on both defect areas and sound areas were analyzed to determine some
an optimal waveband ratio that could be used for further image processing to discriminate defect areas from the sound
tomato surfaces. Unsupervised multivariate analysis method, such as principal component analysis, was also explored to
improve detection accuracy. Threshold values for the optimized features were determined using linear discriminant
analysis. Results showed that tomatoes with defects could be differentiated from the sound ones, with an overall
accuracy of 94.4%. The spectral wavebands and image processing algorithms determined in this study could be used for
multispectral inspection of defects tomatoes.
We have developed nondestructive opto-electronic imaging techniques for rapid assessment of safety and
wholesomeness of foods. A recently developed fast hyperspectral line-scan imaging system integrated with a
commercial apple-sorting machine was evaluated for rapid detection of animal feces matter on apples. Apples
obtained from a local orchard were artificially contaminated with cow feces. For the online trial, hyperspectral
images with 60 spectral channels, reflectance in the visible to near infrared regions and fluorescence emissions with
UV-A excitation, were acquired from apples moving at a processing sorting-line speed of three apples per second.
Reflectance and fluorescence imaging required a passive light source, and each method used independent continuous
wave (CW) light sources. In this paper, integration of the hyperspectral imaging system with the commercial applesorting
machine and preliminary results for detection of fecal contamination on apples, mainly based on the
fluorescence method, are presented.
Emerging concerns about safety and security in current mass production of food products necessitate rapid and reliable inspection for contaminant-free products. Diluted fecal residues on poultry processing plant equipment surface, not easily discernable from water by human eye, are contamination sources for poultry carcasses. Development of sensitive detection methods for fecal residues is essential to ensure safe production of poultry carcasses. Hyperspectral imaging techniques have shown good potential for detecting of the presence of fecal and other biological substances on food and processing equipment surfaces. In this study, use of high spatial resolution hyperspectral reflectance and fluorescence imaging (with UV-A excitation) is presented as a tool for selecting a few multispectral bands to detect diluted fecal and ingesta residues on materials used for manufacturing processing equipment. Reflectance and fluorescence imaging methods were compared for potential detection of a range of diluted fecal residues on the surfaces of processing plant equipment. Results showed that low concentrations of poultry feces and ingesta, diluted up to 1:100 by weight with double distilled water, could be detected using hyperspectral fluorescence images with an accuracy of 97.2%. Spectral bands determined in this study could be used for developing a real-time multispectral inspection device for detection of harmful organic residues on processing plant equipment.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print format on
SPIE.org.