Abstract
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.
Highlights
Over the past 20 years, hyperspectral imaging (HSI) has become an invaluable tool for food safety and quality applications [1,2]
Wavelength bySelection dividing the complete dataset into four disjoint test sets, of which contained voxels from atisleast one fillet of each of the Theeach purpose of wavelength selection to enable classification with a limited corresponding training set for each test set was composed of all data not in the test of wavelengths (3–7) that can be created using optical filters, light emitting diodes (LEDs), etc. to produce a set
The robustness of the proposed simulated annealing was chosen because there was greater variability between fillets of the same species than approach evaluated by fillet
Summary
Over the past 20 years, hyperspectral imaging (HSI) has become an invaluable tool for food safety and quality applications [1,2]. The intentional misrepresentation of food or food ingredients for economic gain, is another major food safety issue that has been addressed with hyperspectral imaging. This technology has been applied for identifying fillets of less expensive species of fish that have been marketed and sold as more expensive red snapper (Lutjanus campechanus) fillets [7,8]. While agriculture applications have remained constant since these early examples, the methods have changed with new technologies enabling more localized analysis. Unmanned aerial vehicles (UAVs) have become attractive survey platforms for local, detailed aerial monitoring efforts [12] and advancements in computing technology and miniaturization of HSI devices have enabled the construction of new systems for in-field crop analysis [13]
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