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Cooking class travel (CCT) is a special form of food economy that is emerging worldwide, especially in Southeast Asian countries, such as Thailand. CCT is a touristic activity with strong value co-creation characteristics, but it is scarcely investigated on how tourists and hosts can work together to co-create the experience and value in CCT. Thus, based on value co-creation theory, this study adopts a rigorous content analysis approach to inspect the value co-creation mechanism of CCT in Thailand. Results reveal a value co-creation framework for CCT—from resources to practices to the final outcomes—offering academia and practitioners an in-depth insight into this fast-developing type of tourism economy and extending value co-creation theory to a new research area.

Food that contains lean meat powder (LMP) can cause human health issues, such as nausea, headaches, and even death for consumers. Traditional methods for detecting LMP residues in meat are often time-consuming and complex and lack sensitivity. This article provides a review of the research progress on the use of surface–enhanced Raman spectroscopy (SERS) technology for detecting residues of LMP in meat. The review also discusses several applications of SERS technology for detecting residues of LMP in meat, including the enhanced detection of LMP residues in meat based on single metal nanoparticles, combining metal nanoparticles with adsorbent materials, combining metal nanoparticles with immunizing and other chemicals, and combining the SERS technology with related techniques. As SERS technology continues to develop and improve, it is expected to become an even more widely used and effective tool for detecting residues of LMP in meat.

Seafood mislabeling rates of approximately 20% have been reported globally. Traditional methods for fish species identification, such as DNA analysis and polymerase chain reaction (PCR), are expensive and time-consuming, and require skilled technicians and specialized equipment. The combination of spectroscopy and machine learning presents a promising approach to overcome these challenges. In our study, we took a comprehensive approach by considering a total of 43 different fish species and employing three modes of spectroscopy: fluorescence (Fluor), and reflectance in the visible near-infrared (VNIR) and short-wave near-infrared (SWIR). To achieve higher accuracies, we developed a novel machine-learning framework, where groups of similar fish types were identified and specialized classifiers were trained for each group. The incorporation of global (single artificial intelligence for all species) and dispute classification models created a hierarchical decision process, yielding higher performances. For Fluor, VNIR, and SWIR, accuracies increased from 80%, 75%, and 49% to 83%, 81%, and 58%, respectively. Furthermore, certain species witnessed remarkable performance enhancements of up to 40% in single-mode identification. The fusion of all three spectroscopic modes further boosted the performance of the best single mode, averaged over all species, by 9%. Fish species mislabeling not only poses health-related risks due to contaminants, toxins, and allergens that could be life-threatening, but also gives rise to economic and environmental hazards and loss of nutritional benefits. Our proposed method can detect fish fraud as a real-time alternative to DNA barcoding and other standard methods. The hierarchical system of dispute models proposed in this work is a novel machine-learning tool not limited to this application, and can improve accuracy in any classification problem which contains a large number of classes.

As an emulsifier and bioactive substance, bile acids (BAs) participate in the absorption of nutrients and in various physiological processes. The objective of this experiment was to investigate the effects of non-12α-hydroxylated BAs (including hyocholic acid, hyodeoxycholic acid and chenodeoxycholic acid, from now on referred to as NBAs) on growth performance, BAs metabolism and the intestinal flora of piglets. The experiment included four groups, with eight piglets per group. The four groups of pigs were fed 0, 60, 120 and 180 mg/kg of NBAs, respectively. The results show that adding NBAs significantly increased the final weight (FW), average daily feed intake (ADFI), average daily gain (ADG), and digestibility of crude fat (EE) and organic matter (OM) in piglets (p < 0.05). Adding NBAs significantly increased the villus height (VH) of the jejunum and ileum (p < 0.05). In addition, NBAs supplementation increased the content of urea nitrogen (BUN) and creatinine (CREA) as well as the ratio of urea nitrogen to creatinine (BUN/CREA) in serum (p < 0.05). Adding NBAs can affect the genes related to BAs enterohepatic circulation. Specifically, adding NBAs significantly decreased the relative mRNA abundance of FXR in the liver (p < 0.05), significantly increased the relative mRNA abundance of CYP27A1 (p < 0.05), and significantly increased the relative mRNA abundance of NTCP (p < 0.05). Adding NBAs also significantly decreased the relative mRNA abundance of FXR in the ileum (p < 0.05). In the full-length 16S rDNA sequencing analysis, ten biomarkers were found from the gate to the species level. NBAs mainly enriched Lactobacillus_Johnsonii and decreased the abundance of Streptococcus_alactolyticus. Short-chain fatty acids (SCFAs) content in the colon was significantly increased (p < 0.05). These results indicate that NBAs supplementation can improve the growth performance of piglets, promote the development of the bile acid replacement pathway and improve intestinal flora.

Abstract Food safety and foodborne diseases are significant global public health concerns. Meat and poultry carcasses can be contaminated by pathogens like E. coli and salmonella, by contact with animal fecal matter and ingesta during slaughter and processing. Since fecal matter and ingesta can host these pathogens, detection, and excision of contaminated regions on meat surfaces is crucial. Fluorescence imaging has proven its potential for the detection of fecal residue but requires expertise to interpret. In order to be used by meat cutters without special training, automated detection is needed. This study used fluorescence imaging and deep learning algorithms to automatically detect and segment areas of fecal matter in carcass images using EfficientNet-B0 to determine which meat surface images showed fecal contamination and then U-Net to precisely segment the areas of contamination. The EfficientNet-B0 model achieved a 97.32% accuracy (precision 97.66%, recall 97.06%, specificity 97.59%, F-score 97.35%) for discriminating clean and contaminated areas on carcasses. U-Net segmented areas with fecal residue with an intersection over union (IoU) score of 89.34% (precision 92.95%, recall 95.84%, specificity 99.79%, F-score 94.37%, and AUC 99.54%). These results demonstrate that the combination of deep learning and fluorescence imaging techniques can improve food safety assurance by allowing the industry to use CSI-D fluorescence imaging to train employees in trimming carcasses as part of their Hazard Analysis Critical Control Point zero-tolerance plan.

Hemorrhage control has been identified as a priority focus area both for civilian and military populations in the United States because exsanguination is the most common cause of preventable death in hemorrhagic injury. Non-compressible torso hemorrhage (NCTH) has high mortality rate and there are currently no broadly available therapies for NCTH outside of a surgical room environment. Novel therapies, which include High Intensity Focused Ultrasound (HIFU) have emerged as promising methods for hemorrhage control as they can non-invasively cauterize bleeding tissue deep within the body without injuring uninvolved regions. A major challenge in the application of HIFU with color Doppler US guidance is the interpretation and optimization of the blood flow images in real-time to identify the hemorrhagic focus. Today, this task requires an expert sonographer, limiting the utility of this therapy in non-clinical environments. In this work, we investigated the feasibility of an automated hemorrhage detection method using a Generative Adversarial Network (GAN) for anomaly detection that learns a manifold of normal blood flow variability and subsequently identifies anomalous flow patterns that fall outside the learned manifold. As an initial feasibility study, we collected ultrasound color Doppler images of femoral arteries in an animal model of vascular injury (N = 11 pigs). Velocity information of the blood flow were extracted from the color Doppler images that were used for training and testing the anomaly detection network. Normotensive images from 8 pigs were used for training, and testing was performed on normotensive, immediately after injury, 10 minutes post-injury and 30 minutes post-injury images from 3 other pigs. The residual images or the reconstructed error maps show promise in detecting hemorrhages with an AUC of 0.90, 0.87, 0.62 immediately, 10 minutes post-injury and 30 minutes post-injury respectively with an overall AUC of 0.83.

Pretension is regarded as an effective way to suppress the draping defects caused by the excessive shearing deformation of preforms during the manufacturing of woven composites. However, the research of shear-pretension coupling behavior on the 3D woven fabric uniform and variable thickness structures has not been carried out. In present study, the behavior of the two kinds of woven fabric structures is investigated. The picture frame shear test for fabrics are conducted with applied pre-tension for the work. The relationship between shear stress and shear angle under different pre-tension levels of the two kinds of fabrics is analyzed. Furthermore, the detailed comparison of shear-pretension coupling behavior between the two kinds of woven fabric structures is also analyzed. Finally, the effect of variable thickness condition on the in-plane shear properties for woven fabric variable thickness structure is investigated. The research provides experimental data support for the forming design of preforms of heterogeneous 3D composite structures.

Consumption of aflatoxin-contaminated food can cause severe illness when consumed by humans or livestock. Because the mycotoxin frequently occurs in cereal grains and other agricultural crops, it is crucial to develop portable devices that can be used non-destructively and in real-time to identify aflatoxin-contaminated food materials during early stages of harvesting or processing. In this study, an aflatoxin detection method was developed using a compact Raman device that can be used in the field. Data were obtained using maize samples naturally contaminated with aflatoxin, and the data were analyzed using a machine learning method. Of the multiple classification models evaluated, such as linear discriminant analysis (LDA), linear support vector machines (LSVM), quadratic discriminant analysis (QDA), and quadratic support vector machines and spectral preprocessing methods, the best classification accuracy was achieved at 95.7% using LDA in combination with Savitzky-Golay 2nd derivative (SG2) preprocessing. Partial least squares regression (PLSR) models demonstrated a close-range accuracy within the scope of standard normal variate (SNV) and multiplicative scatter correction (MSC) preprocessing methods, with determination of coefficient values of R2C and R2V of 0.9998 and 0.8322 respectively for SNV, and 0.9916 and 0.8387 respectively for MSC. This study demonstrates the potential use of compact and automated Raman spectroscopy, coupled with chemometrics and machine learning methods, as a tool for rapidly screening food and feed for hazardous substances at on-site field processing locations.

Identification and segregation of citrus fruit with diseases and peel blemishes are required to preserve market value. Previously developed machine vision approaches could only distinguish cankerous from non-cankerous citrus, while this research focused on detecting eight different peel conditions on citrus fruit using hyperspectral (HSI) imagery and an AI-based classification algorithm. The objectives of this paper were: (i) selecting the five most discriminating bands among 92 using PCA, (ii) training and testing a custom convolution neural network (CNN) model for classification with the selected bands, and (iii) comparing the CNN’s performance using 5 PCA bands compared to five randomly selected bands. A hyperspectral imaging system from earlier work was used to acquire reflectance images in the spectral region from 450 to 930 nm (92 spectral bands). Ruby Red grapefruits with normal, cankerous, and 5 other common peel diseases including greasy spot, insect damage, melanose, scab, and wind scar were tested. A novel CNN based on the VGG-16 architecture was developed for feature extraction, and SoftMax for classification. The PCA-based bands were found to be 666.15, 697.54, 702.77, 849.24 and 917.25 nm, which resulted in an average accuracy, sensitivity, and specificity of 99.84%, 99.84% and 99.98% respectively. However, 10 trials of five randomly selected bands resulted in only a slightly lower performance, with accuracy, sensitivity, and specificity of 98.87%, 98.43% and 99.88%, respectively. These results demonstrate that an AI-based algorithm can successfully classify eight different peel conditions. The findings reported herein can be used as a precursor to develop a machine vision-based, real-time peel condition classification system for citrus processing.

Information technology (IT) and digital marketing are perceived as the driving forces for promoting the development of the hospitality industry in a competitive market. However, there is a global shortage of workers to fill related positions, which hinders the sustainable growth of this industry. Thus, a qualitative thematic approach was adopted in this study to analyze textual data from experts to explore the reasons behind this shortage. Through careful design and analysis, three parties were identified as key generators, including education, industry, and staff/students. Meanwhile, the specific causes made by each party were presented to provide a detailed analysis of the issue. The findings are aimed to help solve the education and human resource issues in the hospitality industry. Nevertheless, the findings could have been more detailed if a follow-up interview was conducted after the thematic analysis.

Glyphosate-based herbicides (GBHs) are the most widely used pesticide worldwide and can provoke placental injury. However, whether and how GBHs damage angiogenesis in the placenta is not yet known. This work evaluated the safety of glyphosate on pregnant sows based on the limit level by governments and investigated the effects and mechanism of Low-GBHs (20 mg/kg) and High-GBHs (100 mg/kg) exposure on placental angiogenesis. Results showed that gestational exposure to GBHs decreased placental vessel density and cell multiplication by interfering with the expression of VEGFA, PLGF, VEGFr2 and Hand2 (indicators of angiogenesis), which may be in relation to oxidative stress-induced disorders of mitochondrial fission and fusion as well as the impaired function of the mitochondrial respiratory chain. Additionally, GBHs destroyed barrier function and nutrient transport in the placenta, and was accompanied by jejunum oxidative stress in newborn piglets. However, GBHs exposure had no significant differences on sow reproductive performance. As a natural antioxidant, betaine treatment protected placenta and newborn piglets against GBHs-induced damage. In conclusion, GBHs impaired placental angiogenesis and function and further damaged the health of postnatal progeny, these effects may be linked to mitochondrial dysfunction. Betaine treatment following glyphosate exposure provided modest relief.

The food service industry must ensure that service facilities are free of foodborne pathogens hosted by organic residues and biofilms. Foodborne diseases put customers at risk and compromise the reputations of service providers. Fluorescence imaging, empowered by state-of-the-art artificial intelligence (AI) algorithms, can detect invisible residues. However, using AI requires large datasets that are most effective when collected from actual users, raising concerns about data privacy and possible leakage of sensitive information. In this study, we employed a decentralized privacy-preserving technology to address client data privacy issues. When federated learning (FL) is used, there is no need for data sharing across clients or data centralization on a server. We used FL and a new fluorescence imaging technology and applied two deep learning models, MobileNetv3 and DeepLabv3+, to identify and segment invisible residues on food preparation equipment and surfaces. We used FedML as our FL framework and Fedavg as the aggregation algorithm. The model achieved training and testing accuracies of 95.83% and 94.94% for classification between clean and contamination frames, respectively, and resulted in intersection over union (IoU) scores of 91.23% and 89.45% for training and testing, respectively, of segmentation of the contaminated areas. The results demonstrated that using federated learning combined with fluorescence imaging and deep learning algorithms can improve the performance of cleanliness auditing systems while assuring client data privacy.

Precise, reliable, and speedy contamination detection and disinfection is an ongoing challenge for the food-service industry. Contamination in food-related services can cause foodborne illness, endangering customers and jeopardizing provider reputations. Fluorescence imaging has been shown to be capable of identifying organic residues and biofilms that can host pathogens. We use new fluorescence imaging technology, applying Xception and DeepLabv3+ deep learning algorithms to identify and segment contaminated areas in images of equipment and surfaces. Deep learning models demonstrated a 98.78% accuracy for differentiation between clean and contaminated frames on various surfaces and resulted in an intersection over union (IoU) score of 95.13% for the segmentation of contamination. The portable imaging system’s intrinsic disinfection capability was evaluated on S. enterica, E. coli, and L. monocytogenes, resulting in up to 8-log reductions in under 5 s. Results showed that fluorescence imaging with deep learning algorithms could help assure safety and cleanliness in the food-service industry.

This study demonstrates a method to select wavelength-specific spectral resolutions to optimize a line-scan hyperspectral imaging method for its intended use, which in this case was visible/near-infrared imaging-based multiple-waveband detection of apple bruises. Many earlier studies have explored important aspects of developing apple bruise detection systems, such as key wavelengths and image processing algorithms. Despite the endeavors of many, development of a real-time bruise detection system is not yet a simple task. To overcome these problems, this study investigated selection of optimal wavelength-specific spectral resolutions for detecting bruises on apples by using hyperspectral line-scan imaging with the Random Track function for non-contiguous partial readout, with two experimental parts. The first part identified key-wavelengths and the optimal number of key-wavelengths to use for detecting low-, medium-, and high-impact bruises on apples. These parameters were determined by principal component analysis (PCA) and sequential forward selection (SFS) with four classification methods. The second part determined the optimal spectral resolution for each of the key-wavelengths by selecting and evaluating 21 combinations of exposure time and key-wavelength bandwidths, and then selecting the best combination based on the bruise detection accuracies achieved by each classification method. Each of the four classification methods was found to have a different optimized resolution for high accuracy bruise detection, and the optimized resolutions also allowed for use of shorter exposure times. The results of this work can be used to help develop multispectral imaging systems that provide rapid, cost-effective post-harvest processing to identify bruised apples on commercial processing lines.

A reservoir area is mostly located in the canyon area, and the geological structure is complex. There are a large number of unstable slopes on the bank of the reservoir. The stability of bank slope is greatly affected by water storage and reservoir water regulation. In addition, sudden rainstorm and other external factors can reduce slope stability. In this article, the physical model test is used to study the seepage field and deformation characteristics of typical reservoir bank slopes with sand layers under different rainfall intensities, different water level fluctuation rates, and their coupling effects. The model has a length of 4.0 m, a width of 1.0 m, and a height of 0.9 m, and the piezometers and white balls are used to monitor the pore water pressures and displacements inside the slope model individually. The results show that the responsiveness of pore water pressure inside the slope lags behind both water level fluctuation and rainfall. The lag time is inversely proportional to the water level fluctuation rates under the single water level fluctuation condition, while it is proportional to water level fluctuation rates in the water level decline stage under the coupling effect condition. The rapid impoundment of the reservoir area has a strengthening effect on the stability of the reservoir bank slope. However, accelerated deformation of the slope occurs in the stage of water level decline, and the deformation rate is proportional to the water level fluctuation rates.

This study is directed towards developing a fast, non-destructive, and easy-to-use handheld multimode spectroscopic system for fish quality assessment. We apply data fusion of visible near infra-red (VIS-NIR) and short wave infra-red (SWIR) reflectance and fluorescence (FL) spectroscopy data features to classify fish from fresh to spoiled condition. Farmed Atlantic and wild coho and chinook salmon and sablefish fillets were measured. Three hundred measurement points on each of four fillets were taken every two days over 14 days for a total of 8400 measurements for each spectral mode. Multiple machine learning techniques including principal component analysis, self-organized maps, linear and quadratic discriminant analyses, k-nearest neighbors, random forest, support vector machine, and linear regression, as well as ensemble and majority voting methods, were used to explore spectroscopy data measured on fillets and to train classification models to predict freshness. Our results show that multi-mode spectroscopy achieves 95% accuracy, improving the accuracies of the FL, VIS-NIR and SWIR single-mode spectroscopies by 26, 10 and 9%, respectively. We conclude that multi-mode spectroscopy and data fusion analysis has the potential to accurately assess freshness and predict shelf life for fish fillets and recommend this study be expanded to a larger number of species in the future.

Hotel websites are vital information channels for potential customers. Hence, their readability and attractiveness play a key role in online marketing for hotels. This study attempts to investigate how hotels use language strategies to better market themselves online. This study first proposes an analytical framework on the basis of appraisal theory, which aims to reveal the hidden mechanism by hotels in building interpersonal relationships, and uses such a framework to explore online language strategies used by five-star hotels in China. The general language strategy and the corresponding preference in the use of such a strategy amongst hotels are identified. Theoretical contributions and managerial implications are generated on the basis of the findings and analysis. It is among the early studies to modify appraisal theory to analyze tourism and hospitality text data and to adopt the corpus approach to explore the hidden mechanism of language use.

Abstract Rapid development of solid-state lighting technology requires new materials with highly efficient and stable luminescence, and especially relies on blue light pumped red phosphors for improved light quality. Herein, we discovered an unprecedented red-emitting Mg2Al4Si5O18:Eu2+ composite phosphor (λ ex = 450 nm, λ em = 620 nm) via the crystallization of MgO–Al2O3–SiO2 aluminosilicate glass. Combined experimental measurement and first-principles calculations verify that Eu2+ dopants insert at the vacant channel of Mg2Al4Si5O18 crystal with six-fold coordination responsible for the peculiar red emission. Importantly, the resulting phosphor exhibits high internal/external quantum efficiency of 94.5/70.6%, and stable emission against thermal quenching, which reaches industry production. The maximum luminous flux and luminous efficiency of the constructed laser driven red emitting device reaches as high as 274 lm and 54 lm W−1, respectively. The combinations of extraordinary optical properties coupled with economically favorable and innovative preparation method indicate, that the Mg2Al4Si5O18:Eu2+ composite phosphor will provide a significant step towards the development of high-power solid-state lighting.

Titanium alloys are widely used in the field of artificial joints, but their poor wear resistance limits their clinical application. Therefore, in this paper, the diamondlike carbon (DLC) film deposited on Ti6Al4V alloy surface by unbalanced magnetron sputtering technology to improve its wear resistance. Swinging tests are conducted on a self-refit multifreedom degree friction tester, and their coefficient of friction, wear loss, and wear morphology were analyzed. Results show that there are no evident scratches on the socket surface with small axial load and angular displacement. The worst wear scratches appear under the largest load and swing angular displacement. A spot of scratches with different depths are found on the edge of the socket, which reveals the characteristic of swinging friction. The friction coefficient increases from 0.134 to 0.206 as the axial load increases, and increases from 0.11 to 0.186 as the swing angular displacement increases. Therefore, the swing angular displacement has greater influence on the wear degree. The wear mechanisms of DLC film involved a combination of fatigue, adhesive, and abrasive wear. [ABSTRACT FROM AUTHOR]

Due to the double disturbance effect of dredging and filling, there will be a problem with weir slope stability after the formation of a fill type saturated clay weir. In order to study the influence of water-level fluctuation on the instability and seepage failure of the dump-fill cofferdam, the saturated-unsaturated seepage theory and strength reduction method are embedded into the finite element system such that the fluid-solid coupling stability of the dump-fill cofferdam is analyzed. The results show that the influence of water-level fluctuation on the instability and seepage failure of the dump-fill cofferdam can be revealed by the coupling analysis excellently. The variation of the saturation line of the cofferdam is consistent with the variation of water-level rising, but the change response of the saturation line of the cofferdam has a lag property during the water-level decline process in that the water level changes faster. The maximum displacement of the cofferdam can be approximated as two stages, namely, the stable growth stage and the accelerated growth stage (slip initiation), in which the influence of the water-level fluctuation on the displacement of stable growth stage is more obvious. Based on the fluid-solid coupling analysis, the most dangerous sliding surface of the cofferdam is located at the position of the horse path, with a critical strength reduction coefficient of approximately 1.475. The possibility of seepage failure of the cofferdam will be increased if the water level exceeds the height of the impermeable wall.

Affected by super-historical flood, the surface deformation of throw filling soft clay cofferdam is obvious and the stability of the cofferdam slope is difficult to ensure. Relying on the cofferdam project of Jiangxi Xinjiang Shuanggang navigation power junction and according to the data of filed investigation and automatic monitoring, the Plaxis infinite element software is used to analyze the deformation characteristics and stabilities of cofferdam during the super-historical flood period, and the effective emergency reinforcement measures are presented. The results show that (1) the stability of cofferdam is controlled by downstream face slope of cofferdam during the super-historical flood period, and there is a most dangerous slide surface interpenetrating through the top to the toe of downstream face slope; (2) the stability of upstream face slope is influenced significantly by the water level fluctuation and it is reduced sharply during the water level decline process; and (3) real-time remote monitoring can effectively reflect the deformation and failure characteristics of cofferdam under the condition of super-historical flood level and provide early warning and prediction in time. The results and analyzing methods can be applied to analyze the similar projects excellently.

Here the authors report a near-infrared K3LuSi2O7:Eu2+ phosphor for non-visible light source application. The selective site occupation of Eu2+ in LuO6 and K2O6 polyhedrons is found to be responsible for the light emission and these insights could serve as design principles for discovery and design of such phosphors.

Relative to standard red/green/blue (RGB) imaging systems, hyperspectral imaging systems offer superior capabilities but tend to be expensive and complex, requiring either a mechanically complex push-broom line scanning method, a tunable filter, or a large set of light emitting diodes (LEDs) to collect images in multiple wavelengths. This paper proposes a new methodology to support the design of a hypothesized system that uses three imaging modes—fluorescence, visible/near-infrared (VNIR) reflectance, and shortwave infrared (SWIR) reflectance—to capture narrow-band spectral data at only three to seven narrow wavelengths. Simulated annealing is applied to identify the optimal wavelengths for sparse spectral measurement with a cost function based on the accuracy provided by a weighted k-nearest neighbors (WKNN) classifier, a common and relatively robust machine learning classifier. Two separate classification approaches are presented, the first using a multi-layer perceptron (MLP) artificial neural network trained on sparse data from the three individual spectra and the second using a fusion of the data from all three spectra. The results are compared with those from four alternative classifiers based on common machine learning algorithms. To validate the proposed methodology, reflectance and fluorescence spectra in these three spectroscopic modes were collected from fish fillets and used to classify the fillets by species. Accuracies determined from the two classification approaches are compared with benchmark values derived by training the classifiers with the full resolution spectral data. The results of the single-layer classification study show accuracies ranging from ~68% for SWIR reflectance to ~90% for fluorescence with just seven wavelengths. The results of the fusion classification study show accuracies of about 95% with seven wavelengths and more than 90% even with just three wavelengths. Reducing the number of required wavelengths facilitates the creation of rapid and cost-effective spectral imaging systems that can be used for widespread analysis in food monitoring/food fraud, agricultural, and biomedical applications.

Contamination inspection is an ongoing concern for food distributors, restaurant owners, caterers, and others who handle food. Food contamination must be prevented, and zero tolerance legal requirements and damage to the reputation of institutions or restaurants can be very costly. This paper introduces a new handheld fluorescence-based imaging system that can rapidly detect, disinfect, and document invisible organic residues and biofilms which may host pathogens. The contamination, sanitization inspection, and disinfection (CSI-D) system uses light at two fluorescence excitation wavelengths, ultraviolet C (UVC) at 275 nm and violet at 405 nm, for the detection of organic residues, including saliva and respiratory droplets. The 275 nm light is also utilized to disinfect pathogens commonly found within the contaminated residues. Efficacy testing of the neutralizing effects of the ultraviolet light was conducted for Aspergillus fumigatus, Streptococcus pneumoniae, and the influenza A virus (a fungus, a bacterium, and a virus, respectively, each commonly found in saliva and respiratory droplets). After the exposure to UVC light from the CSI-D, all three pathogens experienced deactivation (> 99.99%) in under ten seconds. Up to five-log reductions have also been shown within 10 s of UVC irradiation from the CSI-D system.