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Hyperspectral near-infrared reflectance imaging for detection of defect tomatoes
Abstract
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.
... Tomatoes are one of the major fresh-cut vegetables consumed, and the detection of cracking defects is very important to avoid any development of pathogenic microbes that may have harmful consequences on consumer health. Thus, Lee et al. (2011) applied HSI (1000-1700 nm) to detect damaged tomatoes. The authors applied PCA on the full spectrum and four selected wavelengths (1078 nm, 1194 nm, 1425 nm, and 1642 nm) to extract and compare PC images for crack detection. ...
... The authors applied PCA on the full spectrum and four selected wavelengths (1078 nm, 1194 nm, 1425 nm, and 1642 nm) to extract and compare PC images for crack detection. Finally, LDA was applied for improving the discriminant ability between sound and cracked tomatoes, showing a classification accuracy of 91.7% (using full NIR spectrum) and 80.6% (using only four wavelengths) [35]. ...
During the last two decades, hyperspectral imaging (HSI) has been one of the most studied and applied techniques in the field of nondestructive monitoring systems for the fruit and vegetable supply chain. This review provides HSI technical aspects (i.e., device features) and data analysis approaches (i.e., data processing and qualitative/quantitative modeling) for fresh-cut products, focusing on the different applications which the literature offers and the possible scale-up for process monitoring. Moreover, new frontiers in the development of possible process analytical technologies of cost-effective and hand-held HSI devices are presented and discussed. Even though the performance of these new proximal sensing tools needs to be carefully evaluated, new applicative research perspectives in the development of a proximal sensing approach based on HSI sensor networks are ready to be studied and developed for finding field applications (i.e., precision agriculture, food processing, and more) and enabling faster and more convenient analysis while maintaining the accuracy and capabilities of traditional HSI systems.
... At present, there are many methods, such as electronic nose (Mohammad-Razdari, Ghasemi-Varnamkhasti, Yoosefian, Izadi, & Siadat, 2019), biosensor (Caetano & Machado, 2008;Dzyadevych et al., 2004;Pauliukaite, Zhylyak, Citterio, & Spichiger-Keller, 2006), dielectric spectroscopy (De los Reyes, Heredia, Fito, De los Reyes, & Andres, 2007), computer vision technology (Arjenaki, Moghaddam, & Motlagh, 2013;Ireri, Belal, Okinda, Makange, & Ji, 2019), near infrared spectroscopy (NIRS; Qin, Chao, & Kim, 2011;Tiwari, Slaughter, & Cantwell, 2013), hyperspectral/multispectral imaging (Cho et al., 2013;Lee et al., 2011;Mollazade, Omid, Tab, Kalaj, & Mohtasebi, 2018), X-ray imaging (Romero-Dávila & Miranda, 2004), magnetic resonance imaging (Milczarek, Saltveit, Garvey, & McCarthy, 2009), Raman imaging (Qin et al., 2011), for tomato quality evaluation. ...
... Hyperspectral fluorescence imaging technique can be used as an effective classification tool for detecting cracking defects on cherry tomatoes. Lee et al. (2011) used near-infrared HSI technique to detect cuticle cracks on tomatoes. Results showed that tomatoes with defects could be differentiated from the sound ones, with an overall accuracy of 94.4%. ...
Effective detection of tomatoes with early decay still remains one of the major problems in the post‐harvest processing. The feasibility of using visible and near infrared (Vis–NIR) hyperspectral reflectance imaging coupled with image‐spectrum merging processing and analysis technology to detect early decay on tomatoes was observed. Mean normalization method was used to correct the uneven illumination caused by the large curvature change and the reflective characteristics of tomato surface. Principal component (PC) clustering analysis of spectra was used to find the optimal PC that was then used to distinguish sound and decayed tissues of tomatoes. Based on the selected PC, three characteristic wavelengths images at 596, 666, and 858 nm were obtained and the new combination image was calculated. The pseudo‐color image processing and RGB (red, green, and blue) transformation were applied to enhance the contrast between decayed and sound regions on the combined image. G component of RGB image and the corrected single‐wavelength image at 666 nm were used for segmentation of the decayed region and stem‐end on tomatoes, respectively. All samples including 120 decayed and 120 sound tomatoes were used to assess performance of the proposed algorithm. The classification results indicated that 100% of the decayed fruit and 97.5% of sound fruit were accurately identified by proposed algorithm. This study demonstrated that Vis–NIR hyperspectral imaging coupled with image‐spectrum merging processing technology could be a potential approach for detection of tomatoes with early decay.
Practical applications
Decay will not only affect the quality of tomato products, but also cause serious food safety problems. Detection of decayed tomatoes has become an universal attentive and urgent solving question for tomato processing industry. Currently, there was no effective and rapid technology to classify between decayed and sound tomatoes. In this study, a novel method was developed and the decayed tomatoes were successful identified using only three characteristic wavelengths images. This study can provide a useful reference for development of fast multispectral imaging system for decayed tomato detection in practical on‐line grading applications.
... In agricultural applications, hyperspectral imaging was intensively exploited for the detection of fecal contamination, bruise, crack, and damage and for prediction of protein, sugar, fat, and moisture contents in agricultural products (Joshi, Mo, Lee, Lee, & Cho, 2015;Xiong, Sun, Zeng, & Xie, 2014). Also, there are a few notable studies which attempt to detect damages, insect infestation, fungal, viable, stress and varieties and to predict major components such as moisture, protein, oil, starch, sucrose, toxin, and mass using spectroscopic techniques with ranges of 400e2500 nm (Cheng & Sun, 2014;Govender, Chetty, & Bulcock, 2007;Gowen, O'Donnell, Cullen, Downey, & Frias, 2007;Lee et al., 2011;Mo et al., 2015). However, research concerning the detection of virusinfected seeds using spectroscopic techniques and hyperspectral imaging has not been reported yet. ...
The cucurbit diseases caused by cucumber green mottle mosaic virus (CGMMV) have led to a serious problem to growers and seed producers because it is difficult to prevent spreading through pathogen-infected seeds. Conventional detection methods for infected seeds such as biological, serological, and molecular measurements are not practical for measuring entire samples due to their destructive nature, and time, and cost issues. For this reason, it is necessary to develop a rapid and non-destructive novel technique for detecting seeds infestation. A near-infrared (NIR) hyperspectral imaging system was used to discriminate virus-infected seeds from healthy seeds with partial least square discriminant analysis (PLS-DA) and least square support vector machine (LS-SVM). The classification accuracy for virus-infected watermelon seeds were 83.3% with the best model, demonstrating the potential of NIR hyperspectral imaging for detection of virus-infected watermelon seeds.
... In agricultural applications, hyperspectral imaging was intensively exploited for the detection of fecal contamination, bruise, crack, and damage and for prediction of protein, sugar, fat, and moisture contents in agricultural products (Joshi, Mo, Lee, Lee, & Cho, 2015;Xiong, Sun, Zeng, & Xie, 2014). Also, there are a few notable studies which attempt to detect damages, insect infestation, fungal, viable, stress and varieties and to predict major components such as moisture, protein, oil, starch, sucrose, toxin, and mass using spectroscopic techniques with ranges of 400e2500 nm (Cheng & Sun, 2014;Govender, Chetty, & Bulcock, 2007;Gowen, O'Donnell, Cullen, Downey, & Frias, 2007;Lee et al., 2011;Mo et al., 2015). However, research concerning the detection of virusinfected seeds using spectroscopic techniques and hyperspectral imaging has not been reported yet. ...
Background:
There is a need to minimize economic damage by sorting infected seeds from healthy seeds before seeding. However, current methods of detecting infected seeds, such as seedling grow-out, enzyme-linked immunosorbent assays, the polymerase chain reaction (PCR) and the real-time PCR have a critical drawbacks in that they are time-consuming, labor-intensive and destructive procedures. The present study aimed to evaluate the potential of visible/near-infrared (Vis/NIR) hyperspectral imaging system for detecting bacteria-infected watermelon seeds.
Results:
A hyperspectral Vis/NIR reflectance imaging system (spectral region of 400-1000 nm) was constructed to obtain hyperspectral reflectance images for 336 bacteria-infected watermelon seeds, which were then subjected to partial least square discriminant analysis (PLS-DA) and a least-squares support vector machine (LS-SVM) to classify bacteria-infected watermelon seeds from healthy watermelon seeds. The developed system detected bacteria-infected watermelon seeds with an accuracy > 90% (PLS-DA: 91.7%, LS-SVM: 90.5%), suggesting that the Vis/NIR hyperspectral imaging system is effective for quarantining bacteria-infected watermelon seeds.
Conclusion:
The results of the present study show that it is possible to use the Vis/NIR hyperspectral imaging system for detecting bacteria-infected watermelon seeds. © 2016 Society of Chemical Industry.
... Lee et al. previously suggested that an algorithm and statistical methods such as analysis of variance (ANOVA) and principle component analysis could be used to detect cracks on tomatoes [21]. However, these methods were insufficient to detect only cracks from the resulting images because of the remaining specular image. ...
The objective of this study was to evaluate the use of hyperspectral near-infrared (NIR) reflectance imaging techniques for detecting cuticle cracks on tomatoes. A hyperspectral NIR reflectance imaging system that analyzed the spectral region of 1000–1700 nm was used to obtain hyperspectral reflectance images of 224 tomatoes: 112 with and 112 without cracks along the stem-scar region. The hyperspectral images were subjected to partial least square discriminant analysis (PLS-DA) to classify and detect cracks on the tomatoes. Two morphological features, roundness (R) and minimum-maximum distance (D), were calculated from the PLS-DA images to quantify the shape of the stem scar. Linear discriminant analysis (LDA) and a support vector machine (SVM) were then used to classify R and D. The results revealed 94.6% and 96.4% accuracy for classifications made using LDA and SVM, respectively, for tomatoes with and without crack defects. These data suggest that the hyperspectral near-infrared reflectance imaging system, in addition to traditional NIR spectroscopy-based methods, could potentially be used to detect crack defects on tomatoes and perform quality assessments.
Spectroscopic measurement method based on visible and near-infrared wavelengths was prominent technology for rapid and non-destructive evaluation of internal quality of fruits. Reflectance measurement was performed to evaluate firmness, soluble solid content, and acid content of truss tomatoes by hyperspectral reflectance imaging system. The Vis/NIR reflectance spectra was acquired from truss tomatoes sorted by 6 ripening stages. The multivariable analysis based on partial least square (PLS) was used to develop regression models with several preporcessing methods, such as smoothing, normalization, multiplicative scatter correction (MSC), and standard normal variate (SNV). The best model was selected in terms of coefficient of determination of calibration (R_c^2) and full cross validation (R_{cv}^2), and root mean standard error of calibration (RMSEC) and full cross validation (RMSECV). The results of selected models were 0.8976 (R_p^2), 6.0207 kgf (RMSEP) with gaussian filter of smoothing, 0.8379 (R_p^2), 0.2674^{\circ}Bx (RMSEP) with the mean of normalization, and 0.7779 (R_p^2), 0.1033% (RMSEP) with median filter of smoothing for firmness, soluble solid content (SSC), and acid content, respectively. Results show that Vis / NIR hyperspectral reflectance imaging technique has good potential for the measurement of internal quality of truss tomato.
Over the past decade, hyperspectral imaging has been an exciting technology used in many practical fields. In agriculture, hyperspectral imaging is implemented in controlling fruit quality and analyzing specific crops in different estimated climate. Bruise damage is one of the crucial factors that influence significantly quality of fruits. This paper focuses on combining hyperspectral imaging with some supervised classification including k-nearest neighbor (k-NN) and support vector machine (SVM) algorithm to detect bruise areas on pears. Data acquisition of hyperspectral NIR reflectance images of pears was performed for choosing the feature of classification algorithms. Experiment results show the effectiveness of both methods in separating bruise from normal areas in shortest time. These results are good enough to detect the early bruise damage which is not easily recognized by conventional classification methods using human eye.
Postharvest Biology and Technology j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / p o s t h a r v b i o a b s t r a c t The study is based on a hypothesis that internal defects and physiological disorders of fruit lead to changes in tissue thermal properties. During thermal stimulation, heterogeneities of thermal properties lead to the occurrence of thermal contrasts on the surface of fruit material, which can be successfully registered with the use of a thermographic device. A method was developed to detect apple bruising early using pulsed-phase thermography (PPT). In PPT the studied object is heated with an individual thermal pulse (most frequently a rectangular pulse) and the temperature decay on the surface is analysed on a pixel-by-pixel basis as a mixture of harmonic waves, thus enabling the computation of phase and amplitude images. The fast Fourier transform was used to obtain ampligrams and phasegrams of fruit heat response to defects occurring at different depths. The automatic segmentation procedure made it possible to select areas of bruised tissue in thermograms. A comparison of PPT results and visual inspection of bruising was performed, indicating high possibilities of the active thermography method for detecting defects up to several millimetres.
Hyperspectral fluorescence imaging methods were utilized to evaluate the potential detection of pathogenic bacterial biofilm formations on five types of food contact surface materials: stainless steel, high-density polyethylene (HDPE), plastic laminate (Formica), and two variations of polished granite. The main objective of this study was to determine a minimal number of spectral fluorescence bands suitable for detecting microbial biofilms on surfaces commonly used to process and handle food. Spots of biofilm growth were produced on sample surfaces by spot-inoculations of pathogenic Escherichia coli O157:H7 and Salmonella followed by room temperature storage for 3days. Subsequently, hyperspectral fluorescence images were acquired from 421 to 700nm using ultraviolet-A excitation. Both E. coli O157:H7 and Salmonella biofilms emitted fluorescence predominantly in the blue to green wavelengths with emission maxima at approximately 480nm. A single-band image at 559nm was able to detect the biofilm spots on stainless steel. On HDPE and granite, algorithms using different two-band ratios provided better separation of the biofilm spots from background areas than any single-band images did. The biofilm spots on stainless steel, HDPE, and granite could be detected with overall detection rate of 95%. On Formica, too many false positives were present to accurately determine an effective biofilm detection rate. This may have been due to the lower cell population density that was observed for the biofilm spots grown on Formica (approximately 4.3-6.4logcfucm⁻²) as compared to the other surfaces. These findings can be incorporated into developing portable hand-held imaging devices for sanitation inspection of food processing surfaces.
Bacteria have been shown to be present in fresh normal tomatoes. The frequency of their presence differed widely between different fields. Bacterial populations within the tomatoes showed a distinct gradient, being largest in the connective tissue at the stem end and decreasing through the central core towards the peripheral and distal tissue. Application of Serratia to the sepals of young tomato fruits often resulted in their recovery from within tissue of the mature fruit. These findings lend support to the theory that the bacteria enter the fruit through the connective tissue at the stem end.