Multimode Hyperspectral Imaging for Food Quality and Safety

Food safety and quality are becoming progressively important, and a failure to implement monitoring processes and identify anomalies in composition, production, and distribution can lead to severe financial and customer health damages. If consumers were uncertain about food safety and quality, the impact could be profound; hence, we need better ways of minimizing such risks. On the data management side, the rise of artificial intelligence, data analytics, the Internet of Things, and blockchain all provide enormous opportunities for supply chain management and liability management, but the impact of any approach starts with the quality of the relevant data. Here, we present state-of-the-art spectroscopic technologies including hyperspectral reflectance, fluorescence imaging as well as Raman spectroscopy, and speckle imaging that are all validated for food safety and quality applications. We believe a multimode approach comprising of a number of these synergetic optical detection modes is needed for the highest performance. We present a plan where our implementations reflect this concept through a multimode tabletop system in the sense that a large, real-time production-level device would be based on more modes than this mid-level one, while a handheld, portable unit may only address fewer challenges, but with a lower cost and size

Large area periodic, systematically changing, multishape nanostructures by laser interference lithography and cell response to these topographies

The fabrication details to form large area systematically changing multishape nanoscale structures on a chip by laser interference lithography (LIL) are described. The feasibility of fabricating different geometries including dots, ellipses, holes, and elliptical holes in both x- and y- directions on a single substrate is shown by implementing a Lloyd\u27s interferometer. The fabricated structures at different substrate positions with respect to exposure time, exposure angle and associated light intensity profile are analyzed. Experimental details related to the fabrication of symmetric and biaxial periodic nanostructures on photoresist, silicon surfaces, and ion milled glass substrates are presented. Primary rat calvarial osteoblasts were grown on ion-milled glass substrates with nanotopography with a periodicity of 1200 nm. Fluorescent microscopy revealed that cells formed adhesions sites coincident with the nanotopography after 24 h of growth on the substrates. The results suggest that laser LIL is an easy and inexpensive method to fabricate systematically changing nanostructures for cell adhesion studies. The effect of the different periodicities and transition structures can be studied on a single substrate to reduce the number of samples significantly

Inspecting Species and Freshness of Fish Fillets Using Multimode Hyperspectral Imaging Techniques

This study developed multimode hyperspectral imaging techniques to detect substitution and mislabeling of fish fillets. Line-scan hyperspectral images were collected from fish fillets in four modes, including reflectance in visible and nearinfrared (VNIR) region, fluorescence by 365 nm UV excitation, reflectance in short-wave infrared (SWIR) region, and Raman by 785 nm laser excitation. Fish fillets of six species (i.e., red snapper, vermilion snapper, Malabar snapper, summer flounder, white bass, and tilapia) were used for species differentiation and frozen-thawed red snapper fillets were used for freshness evaluation. A total of 24 machine learning classifiers were used for fish species and freshness classifications using four types of spectral data in three different subsets (i.e., full spectra, first ten components of principal component analysis, and bands selected by a sequential feature selection method). The highest accuracies were achieved at 100% using full VNIR reflectance spectra for the species classification and 99.9% using full SWIR reflectance spectra for the freshness classification. The VNIR reflectance mode gave an overall best performance for both species and freshness inspection

Advanced Optical Technologies in Food Quality and Waste Management

Food waste is a global problem caused in large part by premature food spoilage. Seafood is especially prone to food waste because it spoils easily. Of the annual 4.7 billion pounds of seafood destined for U.S. markets between 2009 and 2013, 40 to 47 percent ended up as waste. This problem is due in large part to a lack of available technologies to enable rapid, accurate, and reliable valorization of food products from boat or farm to table. Fortunately, recent advancements in spectral sensing technologies and spectroscopic analyses show promise for addressing this problem. Not only could these advancements help to solve hunger issues in impoverished regions of the globe, but they could also benefit the average consumer by enabling intelligent pricing of food products based on projected shelf life. Additional technologies that enforce trust and compliance (e.g., blockchain) could further serve to prevent food fraud by maintaining records of spoilage conditions and other quality validation at all points along the food supply chain and provide improved transparency as regards contract performance and attribution of liability. In this chapter we discuss technologies that have enabled the development of hand-held spectroscopic devices for detecting food spoilage. We also discuss some of the analytical methods used to classify and quantify spoilage based on spectral measurements

Detection of Fish Fillet Substitution and Mislabeling Using Multimode Hyperspectral Imaging Techniques

Substitution of high-priced fish species with inexpensive alternatives and mislabeling frozen-thawed fish fillets as fresh are two important fraudulent practices of concern in the seafood industry. This study aimed to develop multimode hyperspectral imaging techniques to detect substitution and mislabeling of fish fillets. Line-scan hyperspectral images were acquired from fish fillets in four modes, including reflectance in visible and near-infrared (VNIR) region, fluorescence by 365 nm UV excitation, reflectance in short-wave infrared (SWIR) region, and Raman by 785 nm laser excitation. Fish fillets of six species (i.e., red snapper, vermilion snapper, Malabar snapper, summer flounder, white bass, and tilapia) were used for species differentiation and frozen-thawed red snapper fillets were used for freshness evaluation. All fillet samples were DNA tested to authenticate the species. A total of 24 machine learning classifiers in six categories (i.e., decision trees, discriminant analysis, Naive Bayes classifiers, support vector machines, k-nearest neighbor classifiers, and ensemble classifiers) were used for fish species and freshness classifications using four types of spectral data in three different datasets (i.e., full spectra, first ten components of principal component analysis, and bands selected by sequential feature selection method). The highest accuracies were achieved at 100% using full VNIR reflectance spectra for the species classification and 99.9% using full SWIR reflectance spectra for the freshness classification. The VNIR reflectance mode gave the overall best performance for both species and freshness inspection, and it will be further investigated as a rapid technique for detection of fish fillet substitution and mislabeling

Simulated Annealing-Based Hyperspectral Data Optimization for Fish Species Classification: Can the Number of Measured Wavelengths Be Reduced?

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

Advancements in angular domain optical imaging in biological tissues

Angular Domain Imaging (ADI) is a technique for performing optical imaging through highly scattering media. The basis for the technique is the micro-machined Angular Filter Array (AFA), which provides a parallel collection of micro-tunnels that accept ballistic/quasi-ballistic image-bearing photons and reject multiply scattered photons that result in image-destroying background noise. At high scattering levels, ADI image contrast declines due to the non-uniform scattered background light within the acceptance angle of the AFA. In this thesis, I developed multiple methodologies to correct for this problem and enhance ADI image contrast at higher scattering levels. These methodologies included combining ADI with time gating, polarization gating and employing image processing to estimate the background scattered light and use this information to enhance ADI image contrast and resolution. Furthermore, I conducted a comprehensive experimental investigation on a new AFA geometry designed to reduce the reflections within the micro-tunnels to reduce the unwanted background noise caused by multiply scattered photons. Building on previous studies with ADI in a trans-illumination configuration, I demonstrated that ADI could also be used to capture information-carrying photons from diffuse light back-reflected from tissue, where illumination was from the same side as the AFA. This mode of operation will enable applications of ADI where trans-illumination of samples is not possible. I also developed a tomographic ADI modality that rotated the sample and compiled ADI shadowgrams at each angle into a sinogram, followed by reconstruction of a transverse image with depth information. I also exploited the collimation detection capabilities of the AFA to extract photons emitted by a fluorophore embedded at depth within a turbid medium. The fluorescent imaging system using AFA offered higher resolution and contrast compared to a conventional lens and lens-pinhole fluorescent detection system in both in vitro and animal tests. Optical imaging with an AFA does not depend on coherence of the light source or the wavelength of light. Therefore, it is a promising candidate for multispectral/hyperspectral imaging to localize absorption and/or fluorescence in tissue and may have particular importance in cancer optical imaging