Development of Fluorescence Imaging Technique to Detect Fresh-Cut Food Organic Residue on Processing Equipment Surface

With increasing public demand for ready-to-eat fresh-cut food products, proper sanitation of food-processing equipment surfaces is essential to mitigate potential contamination of these products to ensure safe consumption. This study presents a sanitation monitoring technique using hyperspectral fluorescence images to detect fruit residues on food-processing equipment surfaces. An algorithm to detect residues on the surfaces of 2B-finished and #4-finished stainless-steel, both commonly used in food processing equipment, was developed. Honeydew, orange, apple, and watermelon were selected as representatives since they are mainly used as fresh-cut fruits. Hyperspectral fluorescence images were obtained for stainless steel sheets to which droplets of selected fruit juices at six concentrations were applied and allowed to dry. The most significant wavelengths for detecting juice at each concentration were selected through ANOVA analysis. Algorithms using a single waveband and using a ratio of two wavebands were developed for each sample and for all the samples combined. Results showed that detection accuracies were better for the samples with higher concentrations. The integrated algorithm had a detection accuracy of 100% and above 95%, respectively, for the original juice up to the 1:20 diluted samples and for the more dilute 1:50 to 1:100 samples, respectively. The results of this study establish that using hyperspectral imaging, even a small residual quantity that may exist on the surface of food processing equipment can be detected and that sanitation monitoring and management is possible.


Introduction
Increasing consumption of prepackaged, ready-to-eat fresh-cut foods by people living in busy urban areas has been accompanied by a growing need to ensure food safety and stability, in part due to the occurrence of multiple outbreaks of foodborne illnesses [1]. Although any field-harvested produce can include contaminants such as bacteria, worms, or feces, [2][3][4], microbial growth can occur more easily after fresh-cut processing and thus, fresh-cut products are often more perishable and more vulnerable to pathogens [5]. Incomplete cleaning and sanitation of equipment used to handle these products, such as pumps, tanks, and containers, can lead to cross-contamination of food products. Contamination on the surfaces of food processing equipment is a potential cause of pathogen transmission to finished products of food-processing procedures [6,7]. Food residue remaining on equipment surfaces can shield bacteria from sterilization and drying stress [8]. To reduce

Sample Preparation
Fresh-cut honeydew melon, orange, apple, and watermelon were purchased from local markets. Each fruit was blended and pressed to prepare juice to apply as residue to 2B-finished and #4-finished stainless steel sheets (SSS). For each of the four fruits, six juice concentrations were prepared: first, a stock solution from the initial pressing, followed by five diluted solutions prepared at ratios of 1:5, 1:10, 1:20, 1:50, and 1:100 with distilled water. Fifteen 10-uL droplets of each concentration were applied on the surface of each of the two types of SSS and allowed to dry at ambient room temperature (26 • C for 24 h. For each fruit, 90 droplets (6 sets with 15 droplets) for each of the 6 concentrations were applied onto each of the two types of SSS, for a total of 540 droplets were used for hyperspectral fluorescence imaging.

System and Data Acquisition
The tabletop hyperspectral fluorescence line-scan imaging system used in this study is shown in Figure 1. The hyperspectral fluorescence imaging system comprises a hyperspectral imaging device, transfer table, and ultraviolet-A (UV-A) light source. To identify food components such as chlorophyll a, UV-A (365 nm) excitation light, whose fluorescence emission range is in the blue-to-near-infrared region (up to 730 nm), was used [32]. The imaging device comprises a 14-bit electron-multiplying charge-coupled-device (EMCCD) camera (MegaLuca R EMCCD camera, Andor Technology PLC, Belfast, Northern Ireland), spectrograph (Hyperspec VS imaging spectrograph, Headwall Photonics, Fitchburg, MA, USA) with a 60-µm slit and a C-mount lens (Schneider-Xenoplan 1.4/23 C-mount lens, Schneider Optics, Hauppauge, NY, USA). The transfer table includes a step motor (Velmex, Bloomfield, NY, USA) and linear motion guide. The UV-A light source is composed of two pairs of four 10-W light-emitting diodes (LEDs; LedEngin, San Jose, CA, USA) that emit excitation light at 365-nm [26].
Hyperspectral line-scan images of the juice residues on SSS were obtained by moving the samples by 740 steps at 0.5 nm per step for an exposure time of 0.2 s in a darkened room. Each line-scan contained 502 × 501 spatial pixels obtained by pairwise binning (averaging) of the 1004 × 1002 available pixels in the spatial dimension, and 70 wavebands spanning 450-730 nm in the spectral dimension that were obtained by binning at approximately 4 nm intervals. Since the wavelength region after 730 nm is a region that is repeated as the effect of second order, this region was removed by a filter (Kodak Wratten Gelatin Filter, No. 8, Kodak, Rochester, NY, USA). Through this acquisition process, hyperspectral image cubes composed of 502 × 501 × 70 (spatial × spatial × spectral) were collected using the in-house developed software [10,26].

Sample Preparation
Fresh-cut honeydew melon, orange, apple, and watermelon were purchased from local markets. Each fruit was blended and pressed to prepare juice to apply as residue to 2B-finished and #4-finished stainless steel sheets (SSS). For each of the four fruits, six juice concentrations were prepared: first, a stock solution from the initial pressing, followed by five diluted solutions prepared at ratios of 1:5, 1:10, 1:20, 1:50, and 1:100 with distilled water. Fifteen 10-uL droplets of each concentration were applied on the surface of each of the two types of SSS and allowed to dry at ambient room temperature (26 °C for 24 h. For each fruit, 90 droplets (6 sets with 15 droplets) for each of the 6 concentrations were applied onto each of the two types of SSS, for a total of 540 droplets were used for hyperspectral fluorescence imaging.

System and Data Acquisition
The tabletop hyperspectral fluorescence line-scan imaging system used in this study is shown in Figure 1. The hyperspectral fluorescence imaging system comprises a hyperspectral imaging device, transfer table, and ultraviolet-A (UV-A) light source. To identify food components such as chlorophyll a, UV-A (365 nm) excitation light, whose fluorescence emission range is in the blue-to-near-infrared region (up to 730 nm), was used [32]. The imaging device comprises a 14-bit electron-multiplying charge-coupled-device (EMCCD) camera (MegaLuca R EMCCD camera, Andor Technology PLC, Belfast, Northern Ireland), spectrograph (Hyperspec VS imaging spectrograph, Headwall Photonics, Fitchburg, MA, USA) with a 60-μm slit and a C-mount lens (Schneider-Xenoplan 1.4/23 C-mount lens, Schneider Optics, Hauppauge, NY, USA). The transfer table includes a step motor (Velmex, Bloomfield, NY, USA) and linear motion guide. The UV-A light source is composed of two pairs of four 10-W light-emitting diodes (LEDs; LedEngin, San Jose, CA, USA) that emit excitation light at 365-nm [26].
Hyperspectral line-scan images of the juice residues on SSS were obtained by moving the samples by 740 steps at 0.5 nm per step for an exposure time of 0.2 s in a darkened room. Each line-scan contained 502 × 501 spatial pixels obtained by pairwise binning (averaging) of the 1004 × 1002 available pixels in the spatial dimension, and 70 wavebands spanning 450-730 nm in the spectral dimension that were obtained by binning at approximately 4 nm intervals. Since the wavelength region after 730 nm is a region that is repeated as the effect of second order, this region was removed by a filter (Kodak Wratten Gelatin Filter, No. 8, Kodak, Rochester, NY, USA). Through this acquisition process, hyperspectral image cubes composed of 502 × 501 × 70 (spatial × spatial × spectral) were collected using the in-house developed software [10,26].

Data Analysis
The collected hyperspectral fluorescence images were calibrated using a dark reference image, and a flat-field image shows that fluorescence was exhibited uniformly [33].
where I is the corrected relative hyperspectral fluorescence image, Is is the sample hyperspectral fluorescence image, Ir is the hyperspectral fluorescence image of the fluorescent reference plate, and D is the hyperspectral image of the dark reference plate, all at the i-th wavelength. The fluorescence spectra of the pixels composed of fruit juice residues and the SS surfaces were extracted from the corrected fluorescence hyperspectral images and used to calculate the average fluorescence spectrum. The spectra were extracted from the region of interest (ROI) of the juice residue spots on the SSS in the corrected fluorescence image. The ROI comprises the region from the center to the boundary of the residue, and 15-25 pixels per droplet were selected in this region.
As shown in Figure 2, algorithms were developed for detecting residues using a single waveband (SW) and a ratio of two wavebands. Data analysis was performed as follows. The SW algorithms were developed using the fluorescence intensity of one waveband, and the two waveband ratio (TWR) algorithms were developed using the ratio of the fluorescence intensity of two wavebands. The optimal wavebands for identifying the residues of each fruit and the SSS were determined via one-way analysis of variance (ANOVA) of the extracted spectra. The spectra extracted from the ROI were divided into two groups, namely, clean surface and residue, and labelled as 0 and 1, respectively. Oneway ANOVA was performed on all 70 wavebands in the 450-730 nm region to differentiate between the two groups. The F-value obtained via the ANOVA analysis for an SW and the ratio of two wavebands was used to select the optimal wavebands [22,30]. A larger F-value represents a more statistically significant mean separation between the two groups.
Optimal bands were selected using the calibration data set for the six dilution concentrations of each fruit residue on two types of SS surfaces. The SW algorithms and TWR algorithms were developed using the selected optimal band, and these models were verified using a validation set. The data sets for the calibration and validation were randomly divided into 80% and 20% of the total data, respectively.
The calibration algorithm determines the threshold value that has the highest residue detection accuracy in the calibration data set, and the detection accuracy of the validation model is determined by applying this threshold value to the validation data set. In addition, a global detection algorithm was developed that can be applied to the residues of all four types of fruits.
The global residue detection image algorithm was developed by applying the developed global detection algorithm to the hyperspectral fluorescence images. A two waveband ratio image (TWRI) algorithm was developed using the ratio image of two wavebands selected in the TWR algorithm according to Equation (2).
where I a and I b denote the corrected images at wavebands a and b, respectively. Imaging algorithms were developed to use an SW and a TWR to detect juice residue on the SS surfaces. A binary image composed of black of a clean surface with '0' and white of the residue with '1' was generated by applying the threshold value to the image with the selected waveband, and the regions in white were identified as residues. The detection accuracy of the residues refers to the number of positives wherein the residues are correctly identified. Image processing, data extraction, and analysis were performed using MATLAB (Version R2016b, MathWorks, Inc., Natick, MA, USA, 2016). Appl. Sci. 2021, 11, x FOR PEER REVIEW 5 of 20 Figure 2. Flowchart for the development of the optimal residual-organic detection algorithm.

Fluorescence Characteristics of Stainless Steel and Fruits Samples
Generally, 2B-finished stainless-steel has a smooth surface and slight reflection similar to a mirror, while #4-finished stainless steel has a fine-polishing grit line with uniform appearance and directionality on its brushed surface [34]. Figure 3 shows the fluorescence spectra of the two types of stainless steel. The 2B spectrum has a characteristic broad fluorescence peak in the 600-740 nm range, while the #4 spectrum is relatively flat at all wavelengths across the 450-730 nm range.

Fluorescence Characteristics of Stainless Steel and Fruits Samples
Generally, 2B-finished stainless-steel has a smooth surface and slight reflection similar to a mirror, while #4-finished stainless steel has a fine-polishing grit line with uniform appearance and directionality on its brushed surface [34]. Figure 3 shows the fluorescence spectra of the two types of stainless steel. The 2B spectrum has a characteristic broad fluorescence peak in the 600-740 nm range, while the #4 spectrum is relatively flat at all wavelengths across the 450-730 nm range.

Fluorescence Characteristics of Stainless Steel and Fruits Samples
Generally, 2B-finished stainless-steel has a smooth surface and slight reflection similar to a mirror, while #4-finished stainless steel has a fine-polishing grit line with uniform appearance and directionality on its brushed surface [34]. Figure 3 shows the fluorescence spectra of the two types of stainless steel. The 2B spectrum has a characteristic broad fluorescence peak in the 600-740 nm range, while the #4 spectrum is relatively flat at all wavelengths across the 450-730 nm range.   Figure 4 shows, for each fruit, the average spectra for each concentration of juice residue on the surfaces of 2B-finished SSS. All the samples show a broad fluorescence peak in the 600-740 nm region similar to that of the 2B-finished stainless steel spectrum in Figure 3.
The honeydew residue spectra show a tendency for fluorescence peak intensities in the 450-600 nm range to decrease with increasing dilution (decreasing juice concentration); the most diluted samples (1:100) show nearly the same fluorescence properties as 2B stainless steel in this region. In the 600-730 nm range, the fluorescence peaks of stainless steel are the highest, and the 1:5, 1:20, 1:50, and 1:100 dilutions also exhibit fluorescence properties similar to those of the 2B stainless steel.
Similarly, the orange residue spectra show a decrease in fluorescence peak intensity with the increasing dilutions for the 450-600 nm range. Again, in the 600-730 nm region, the peak values of 2B stainless steel are the highest, and there are almost no differences in the fluorescence peaks for all the dilution concentrations.
The apple residue spectra also show a decrease in the fluorescence peak intensity with the increasing dilutions for the 450-600 nm range; the values for the 1:5 and 1:10 samples increase, whereas the remaining samples show fluorescence properties similar to those of stainless steel. In the 600-730 nm region, the fluorescence peak of stainless steel is the highest, and there are differences between the fluorescence peaks exhibited by the original juice and the 1:5 dilution concentration. However, there are almost no differences among the fluorescence peaks at the other dilution concentrations.
The watermelon residue spectra also show a decrease in fluorescence peak intensities in the 450-600 nm range with increasing dilution; the values for the 1:5, 1:10, and 1:20 samples increase, whereas the remaining samples exhibit fluorescence properties similar to those of stainless-steel. In the 600-730 nm range, the peak value of stainless steel is the highest, and there are almost no differences among the fluorescence peaks at all the dilution concentrations.
On the 2B finished surface, all the fruits used in the experiment show significant differences in the spectra for the different concentrations in the 450-600 nm range. In the 600-730 nm range, the peak value of stainless-steel is the highest for all four fruits, and it is difficult to distinguish based on the dilution concentration. The 450-600 nm and 600-730 nm ranges are known to be related to carotenoids and chlorophyll characteristics, respectively [35][36][37]. Figure 5 shows, for each fruit, the average spectra for each concentration of juice residue on the surfaces of #4-finished SSS. For all the fruits, the original juice exhibits the highest fluorescence intensities and the #4 surface has much less influence on the juice residue spectra in Figure 5 compared to the influence of the 2B surface in the juice residue spectra in Figure 4. In the 450-600 nm range, all the samples have fluorescence properties similar to those of #4-finished SSS. In the 650-690 nm range, the original juice, and the 1:5 and 1:10 dilution samples exhibit intensities much higher than the #4 SSS does, whereas the spectra of the remaining dilution samples are more similar in intensity and in their trend with concentration.
In the 450-600 nm range, the spectra of the honeydew residue on #4 SSS show a decrease in fluorescence peak intensities with each sequential dilution of the original juice. Although the spectra of the original juice and the first three dilutions are very clearly distinguishable, but those of the two most dilute samples (1:50 and 1:100) are not. In the 600-730 nm range, the original juice and the 1:5 dilution can be distinguished but the four remaining dilutions cannot.
The spectra of the orange residue on #4 SSS show decreasing fluorescence intensity with increasing dilution in the 450-600 nm range, similar to that of the honeydew residue spectra. In the 600-730 nm range, clearly distinguishable fluorescence peaks are observed for the original juice and for the 1:50 and 1:100 dilution samples.    The spectra of apple residue on #4 SSS show a similar pattern of decreasing fluorescence intensity with increasing dilution in the 450-600 nm range, with the original juice and the 1:5 and 1:10 dilutions being the most distinct. The remaining samples have spectral properties similar to those of stainless steel. In the 600-730 nm range, the high intensity fluorescence peak decreases as the dilution increases for the six juice concentrations.
The spectra of watermelon residue on #4 SSS show a similar pattern to those of honeydew and orange in the 450-600 nm range, with decreasing fluorescence peak intensities with each sequential dilution of the original juice and distinguishable spectra for the original juice and the first three dilutions, but non-distinguishable spectra for the two most dilute samples (1:50 and 1:100). In the 600-730 nm range, the original juice and the 1:10 dilution clearly show a high intensity spectral peak but the spectra of the four remaining dilutions are not distinguishable.
All the fruits show features in common on the 2B-finished as well as #4-finished surfaces. The broad peaks in the 450-600 nm range are presumed to be due to the complex composition of components that affect sugar content, such as glucose, or the components of flavonoids and carotenoids. The peak between 600-730 nm may to be due to chlorophyll [6,35,[38][39][40][41][42][43].

Optimal Waveband Selection Using ANOVA Analysis Results
ANOVA analysis was performed according to the two types of SS surfaces and fruit juice dilution levels in order to select the optimal wavebands for detecting residue contamination. Figures S1-S6 show the F-value of the one-way ANOVA for classifying two types of SS surfaces and four types of fruit residues using the SW and the TWR. The optimal wavebands and classification results for each algorithm in terms of residue detection using the pixel spectra are shown in Tables 1-4.     Figure S1a shows F-value of the one-way ANOVA for classifying 2B finished SS surface and honeydew residues. The resultant optimal wavebands for the original juice, and the 1:5, 1:10, and 1:20 dilution samples were close to 480 nm, and the threshold values for detecting the residues using the selected wavebands were 1983, 827, 545, and 362, respectively ( Figure S1). The calibration accuracies were 92.92%, 91.56%, 86.86%, and 86.33%, and the validation accuracies were 92.94%, 91.45%, 86.99%, and 83.99%, respectively. The optimal wavebands for the 1:50 and 1:100 dilution samples were 684.4 nm, and the threshold values were 1742 and 1563, respectively. The calibration accuracies were 75.78% and 69.08%, and the validation accuracies were 70.46% and 67.25%, respectively. The 480-nm and 684-nm wavebands are related to carotenoids and chlorophyll, respectively [35][36][37].
According to the SWA developed to detect orange residue on the surface of #4-finished SSS, the optimal waveband for the original juice was 492 nm, the threshold value was 620, the calibration accuracy was 98.23%, and the validation accuracy was 98.10% ( Figure S2b). The optimal waveband for the 1:5, 1:10, and 1:20 dilution samples was 464 nm, and the threshold values were 1422, 1003, and 454, respectively. The calibration accuracies were 91.15%, 91.47%, and 92.98%, and the validation accuracies were 98.10%, 91.37%, and 91.66%, respectively. The optimal wavebands for the 1:50 and 1:100 dilution samples were 660 nm, and the threshold values were 577 and 994, respectively. The calibration accuracies were 92.72% and 89.81%, and the validation accuracies were 92.85% and 89.75%, respectively. 492-nm and 660-nm were related to carotenoids and chlorophyll-b, respectively [36,37].

Residue Detection Algorithm for Apple
For the SWA developed for detecting apple residue on the surface of 2B-finished SSS, the optimal waveband for the original juice, and the 1:5 and 1:10 dilution samples was 488 nm, and the threshold values for detecting the residues using the selected wavebands were 481, 415, and 382, respectively ( Figure S1c). The calibration accuracies were 93.38%, 86.16% and 85.16% and the validation accuracies were 93.37%, 85.29% and 83.86%, respectively. The optimal wavebands for the 1:20, 1:50, and 1:100 dilution samples were close to 648 nm, and the threshold values for detecting the residues using the selected wavebands were 1707, 1556, and 1825, respectively. The 488-nm and 648-nm wavebands were related to carotenoids and chlorophyll-b, respectively [36,37].
For the SWA developed for detecting apple residue on the surface of #4-finished SSS, the optimal waveband for the original juice was 584 nm, the threshold value was 677, the calibration accuracy was 85.81%, and the validation accuracy was 85.17% ( Figure S2c). The optimal wavebands for the 1:5 and 1:10 dilution samples were close to 470 nm, and the threshold values were 422 and 344, respectively. The calibration accuracies were 82.48% and 83.55%, and the validation accuracies were 79.47% and 72.34%, respectively. The optimal wavebands for the 1:20, 1:50, and 1:100 dilution samples were close to 660 nm, and the threshold values were 338, 437, and 487, respectively. The calibration accuracies were 80.63%, 71.83%, and 70.02%, and the validation accuracies were 77.48%, 70.04%, and 69.06%, respectively. The 470-nm and 660-nm wavebands were related to carotenoids and chlorophyll-b, respectively [36,37].
Tables 1 and 2 summarize the results of the ANOVA analysis performed to determine the optimal wavelengths to detect different samples. First, when the SWA was applied, the optimal wavebands for each of the dilution samples were similarly selected on the surfaces of 2B-finished SSS and #4-finished SSS. For honeydew, orange, and watermelon, the optimal wavebands for the original juice and the 1:5, 1:10, and 1:20 dilution samples were in the 470-500 nm range, and the optimal wavebands for the 1:50 and 1:100 dilution samples were in the 660-680 nm range. For apple, the optimal wavebands for original juice, and the 1:5 and 1:10 dilution samples were in the 480-490 nm range, and the optimal wavebands for the 1:20, 1:50, and 1:100 dilution samples were in the 648-660 nm range. The lower the dilution levels on both surfaces, the lower was the accuracy of the selected waveband.
In order to determine whether the accuracy was higher when using a single waveband or multiple wavebands, the optimal wavebands were selected using the ratio algorithm (Tables 3 and 4). Based on the TWRA developed to detect orange residue on the surface of #4 finished SSS, the optimal wavebands for original juice, and the 1:5, 1:10 and 1:20 dilution samples were in the 450-470 nm and 610-660 nm ranges ( Figure S8). The TWRA calibration accuracies were 97.98%, 91.03%, 90.63%, and 89.03% with threshold values of 0.29, 2.06, 1.64, and 1.13, and validation accuracies of 97.86%, 91.94%, 89.28%, and 89.94%, for these samples. The optimal wavebands for the 1:50 and 1:100 dilution samples were in the 560-608 nm and 664-676 nm ranges. The TWRA calibration accuracies were 88.35% and 88.14% with threshold values of 0.22 and 0.50, and validation accuracies of 88.14% and 87.73%, respectively.
For the TWRA developed for detecting apple residue on the surface of #4-finished SSS, the optimal wavebands for the original juice were 564 nm and 676 nm ( Figure S9). The TWRA calibration accuracy was 84.12% with a threshold value of 0.14 and validation accuracy of 84.18%. The optimal wavebands for the 1: For the TWRA developed for detecting watermelon residue on the surface of #4finished SSS, the optimal wavebands for the original juice and dilution samples up to 1:20 were 460-490 nm, 728 nm, and 580-616 nm ( Figure S10). The TWRA calibration accuracies were 96.53%, 90.52%, 89.31%, and 88.59% with threshold values of 0.59, 1.08, 0.55, and 0.55, and validation accuracies of 96.66%, 84.17%, 89.81%, and 89.15%, respectively. The optimal wavebands for the 1:50 and 1:100 dilution samples were in the 548-580 nm and 660-676 nm ranges. The TWRA calibration accuracies were 82.60% and 74.52% with threshold values of 2.48 and 1.0, and validation accuracies of 82.84% and 76.86%, respectively.
Similar to the SWA results, the lower was the dilution concentration on both surfaces, the lower was the accuracy of the selected waveband. In the TWRA, consistent wavebands were selected for the 2B -finished SS surface. The detection accuracy for the original juices was high; however, the accuracy tended to decrease with juice concentration.

Development of Global Imaging Algorithm for Detecting Residues
Based on these results, an algorithm was developed that allows the selected wavelengths to be applied for concentration, regardless of the type of fruit. The SWA was initially applied (Table 5). On the 2B-finished SS surface, a detection accuracy of 93.37% was obtained when a threshold value of 482 was applied at 488 nm for the original juices. Detection accuracies of 92.42%, 90.91%, and 83.34% were obtained when threshold values of 670, 545, and 382 were applied, respectively, at 484 nm for the 1:5 up to 1:20 dilution samples, respectively. For the 1:50 and 1:100 dilution samples, detection accuracies of 62.51% and 55.81% were obtained when thresholds values of 1633 and 1685 at 644 nm, were applied, respectively. On the #4-finished SS surface, the original juices were detected at an accuracy of 84.43%, when a threshold value of 670 was applied at 460 nm. Detection accuracies of 90.74%, 83.31%, and 75.71% were obtained when the threshold values of 611, 594, and 549 were applied at 484 nm for the 1:5 up to 1:20 dilution samples, respectively. For the 1:50 dilution sample at 652 nm and the 1:100 dilution sample at 660 nm, detection accuracies of 68.41% and 80.95% were obtained when threshold values of 301 and 332 were applied, respectively. For the detection of fruit residue, the 460 nm and 484 nm wavelengths related to carotenoids were selected for a 1:20 dilution concentration of the original juice, and 644-660 nm related to chlorophyll was selected for a dilution concentration of 1:50-1:100 [35][36][37].  Table 6 shows the results of applying the integrated wavebands using the TWRA. The difference between the wavebands selected at each dilution concentration and the wavebands selected at the total dilution concentration on the 2Bfinished SS surface was not greater than that on the #4-finished SS surface. Therefore, the integrated algorithms for the 2B-finished SS surface exhibited better detection accuracies than those for the #4-finished SS surface.  Figure 6 shows an example of the process in which an image is applied at the selected wavelength using ANOVA analysis. The threshold value determined using the TWRA is applied after acquiring the ratio image with the selected single-wavelength images. The obtained binary image renders it possible to determine whether the residues are detected by expressing the part recognized as detected in white and the part recognized as missing in black.
An imaging algorithm to detect fruit residue contamination was developed using the waveband image selected through ANOVA analysis. Figure 6 illustrates the image processing that was performed to detect residues using a two-band ratio image. If a residue is present, the binary image is '1', otherwise it is '0'. Figure 7 shows the determination of residue contamination based on the concentration for four types of fruit. These results establish the possibility of using fluorescence imaging techniques to detect organic residues that may be present in food processing equipment, even after cleaning and sterilization.  Figure 6 shows an example of the process in which an image is applied at the selected wavelength using ANOVA analysis. The threshold value determined using the TWRA is applied after acquiring the ratio image with the selected single-wavelength images. The obtained binary image renders it possible to determine whether the residues are detected by expressing the part recognized as detected in white and the part recognized as missing in black.
An imaging algorithm to detect fruit residue contamination was developed using the waveband image selected through ANOVA analysis. Figure 6 illustrates the image processing that was performed to detect residues using a two-band ratio image. If a residue is present, the binary image is '1', otherwise it is '0'. Figure 7 shows the determination of residue contamination based on the concentration for four types of fruit. These results establish the possibility of using fluorescence imaging techniques to detect organic residues that may be present in food processing equipment, even after cleaning and sterilization.     Table 7 shows the results of image discrimination applied with the integrated TWRA. The discrimination accuracy of the integrated image model for the 2B-finished SSS was 100%, 99.17%, and 95% for original juices up to 1:20 dilution samples, 1:50 dilution samples, and 1:100 dilution samples, respectively. The discrimination accuracy of the integrated image model for the #4-finished SSS was 100%, 94.17%, 93.89%, and 89.44% for original juices up to 1:10 dilution samples, 1:20 dilution samples, 1:50 dilution samples, and 1:100 dilution samples, respectively. The image-based discrimination result showed better performance than the spectrum-based discrimination result. Also, residue detection performance was better for the 2B-finished SS surface than for the #4-finished SS surface. the detection accuracy on the 2B-finished surface was higher than that on the #4-finished surface. Based on these results, an algorithm will be developed that can be applied to organic residue of various fresh-cut fruits and vegetables in the future.
The results of this study establish the possibility of using fluorescence imaging techniques to detect residues that may be present in food processing equipment, even after cleaning and sterilization [6]. Fluorescence imaging can be applied for testing the presence of organic residues of various agricultural products by detecting microgram quantities. In addition, the results show that sanitation monitoring for residues is possible. Therefore, the results of this study can be applied to the development of a multispectral imaging system to detect the residue of fresh-cut foods in food processing plants.