Tom C. Pearson

Reflectance and Transmittance Spectroscopy Applied to Detecting Fumonisin in Single Corn Kernels Infected with Fusarium verticillioides

ABSTRACT Reflectance and transmittance visible and near-infrared spectroscopy were used to detect fumonisin in single corn kernels infected with Fusarium verticillioides. Kernels with >100 ppm and <10 ppm could be classed accurately as fumonisin positive or negative, respectively. Classification results were generally better for oriented kernels than for kernels that were randomly placed in the spectrometer viewing area. Generally, models based on reflectance spectra have higher correct classification than models based on transmittance spectra. Statistical analyses indicated that including near-infrared wavelengths in calibrations improved classifications, and some calibrations were improved by including visible wavelengths. Thus, the color and chemical constituents of the infected kernel contribute to classification models. These results show that this technology can be used to rapidly and nondestructively screen single corn kernels for the presence of fumonisin, and may be adaptable to on-line detection...

Feasibility of impact-acoustic emissions for detection of damaged wheat kernels

A non-destructive, real time device was developed to detect insect damage, sprout damage, and scab damage in kernels of wheat. Kernels are impacted onto a steel plate and the resulting acoustic signal analyzed to detect damage. The acoustic signal was processed using four different methods: modeling of the signal in the time-domain, computing time-domain signal variances and maximums in short-time windows, analysis of the frequency spectrum magnitudes, and analysis of a derivative spectrum. Features were used as inputs to a stepwise discriminant analysis routine, which selected a small subset of features for accurate classification using a neural network. For a network presented with only insect damaged kernels (IDK) with exit holes and undamaged kernels, 87% of the former and 98% of the latter were correctly classified. It was also possible to distinguish undamaged, IDK, sprout-damaged, and scab-damaged kernels.

CLASSIFICATION OF CLOSED- AND OPEN-SHELL PISTACHIO NUTS USING VOICE-RECOGNITION TECHNOLOGY

An algorithm using speech recognition technology was developed to distinguish pistachio nuts with closed shells from those with open shells. It was observed that upon impact with a steel plate, nuts with closed shells emit different sounds than nuts with open shells. Features extracted from the sound signals consisted of mel-cepstrum coefficients and eigenvalues obtained from the principle component analysis (PCA) of the autocorrelation matrix of the sound signals. Classification of a sound signal was performed by linearly combining the mel-cepstrum and PCA feature vectors. An important property of the algorithm is that it is easily trainable, as are most speech-recognition algorithms. During the training phase, sounds of nuts with closed shells and with open shells were used to obtain a representative vector of each class. During the recognition phase, the feature vector from the sample under question was compared with representative vectors. The classification accuracy of closed-shell nuts was more than 99% on the validation set, which did not include the training set.

An Overview of Signal Processing for Food Inspection [Applications Corner]

The goal of this article is to introduce the signal processing community to the challenges that arise in food inspection. We briefly describe both traditional food-inspection technologies, which rely on sample collection and subsequent offline analysis in a laboratory, and newer approaches that use nondestructive methods to measure various quality parameters of food products in real time. We focus on four specific examples to illustrate the breadth of technologies currently in use in food inspection and the challenges that remain to be addressed. In each case, we describe the problem setting and its economic and health aspects; the techniques that are used, including the physical principles on which these techniques are based; and their performance and cost

DETECTION OF PISTACHIO NUTS WITH CLOSED SHELLS USING IMPACT ACOUSTICS

An acoustical sorting system was developed to separate pistachio nuts with closed shells from those with open shells. The system includes a microphone, digital signal processing hardware, material handling equipment, and an air reject mechanism. It was found that upon impact with a steel plate, nuts with closed shells emit sound with higher signal magnitudes for the first 0.33 ms than do nuts with open shells. After this interval, nuts with closed shells emit sounds with lower signal magnitudes than those with open shells. Linear discriminant analysis was used to classify nuts using three features extracted from the microphone signal during the first 1.4 ms after impact. One of the discriminant features is the integrated absolute value of microphone output signal during the first 0.11 ms after impact. The other two features are the number of data points in the digitized microphone signal, between 0.6 and 1.4 ms after impact, that have a slope and signal magnitude below preset threshold levels. The classification accuracy of this system is approximately 97%. Throughput rate is approximately 40 nuts/s. Cost is about

Detecting Vitreous Wheat Kernels Using Reflectance and Transmittance Image Analysis

7,000 to

Detection of Fungus-Infected Corn Kernels using Near-Infrared Reflectance Spectroscopy and Color Imaging

10,000 per channel. This cost is much lower than that of color sorters used to remove other pistachio defects while throughput is comparable. Currently, closed–shell pistachio nuts are removed by mechanical devices. These devices have a lower classification accuracy (95%) and damage kernels in open–shell pistachios by “pricking” them with a needle. The needle hole can give the appearance of an insect tunnel and cause rejection by the consumer. The newly developed system does not cause such damage. Increased sorting accuracy of the acoustic sorter, coupled with low cost, enables a payback period of less than one year.

Automated detection of pistachio defects by machine vision

ABSTRACT The proportion of vitreous durum kernels in a sample is an important grading attribute in assessing the quality of durum wheat. The current standard method of determining wheat vitreousness is performed by visual inspection, which can be tedious and subjective. The objective of this study was to evaluate an automated machine-vision inspection system to detect wheat vitreousness using reflectance and transmittance images. Two subclasses of durum wheat were investigated in this study: hard and vitreous of amber color (HVAC) and not hard and vitreous of amber color (NHVAC). A total of 4,907 kernels in the calibration set and 4,407 kernels in the validation set were imaged using a Cervitec 1625 grain inspection system. Classification models were developed with stepwise discriminant analysis and an artificial neural network (ANN). A discriminant model correctly classified 94.9% of the HVAC and 91.0% of the NHVAC in the calibration set, and 92.4% of the HVAC and 92.7% of the NHVAC in the validation set...

Detection of underdeveloped hazelnuts from fully developed nuts by impact acoustics

Contamination of grain products by fungus can lead to economic losses and is deleterious to human and livestock health. Detection and quantification of fungus-infected corn kernels would be advantageous for producers and breeders in evaluating quality and in selecting hybrids with resistance to infection. This study evaluated the performance of single-kernel near-infrared reflectance spectroscopy (NIRS) and color imaging to discriminate corn kernels infected by eight fungus species at different levels of infection. Discrimination was done according to the level of infection and the mold species. NIR spectra (904 to 1685 nm) and color images were used to develop linear and nonlinear prediction models using linear discriminant analysis (LDA) and multi-layer perceptron (MLP) neural networks. NIRS was able to accurately detect 98% of the uninfected control kernels, compared to about 89% for the color imaging. Results for detecting all levels of infection using NIR were 89% and 79% for the uninfected control and infected kernels, respectively; color imaging was able to discriminate 75% of both the control and infected kernels. In general, there was better discrimination for control kernels than for infected kernels, and certain mold species had better classification accuracy than others when using NIR. The vision system was not able to classify mold species well. The use of principal component analysis on image data did not improve the classification results, while LDA performed almost as well as MLP models. LDA and mean centering NIR spectra gave better classification models. Compared to the results of NIR spectrometry, the classification accuracy of the color imaging system was less attractive, although the instrument has a lower cost and a higher throughput.

DETERMINING VITREOUSNESS OF DURUM WHEAT USING TRANSMITTED AND REFLECTED IMAGES

Pistachio (Pistacia vera) nuts with shell and kernel defects detract from consumer acceptance and, in some cases, may be more prone to insect damage, mold decay, and/or aflatoxin contamination. The objective of this study was to develop imaging algorithms to improve sorting of nuts with the following shell defects: oily stains, dark stains, adhering hull, and the following kernel defects: navel orangeworm (NOW) damage, fungal decay, and Aspergillus molds, all of which indicate risk of aflatoxin contamination. Imaging algorithms were developed to distinguish normal nuts from those nuts with oily stains, dark stains, and adhering hull as well as nuts having kernel defects. Image algorithm testing on a validation data set showed that nuts having oily stain, dark stain, or adhering hull could be distinguished from normal nuts with an accuracy of 98%. Removing nuts with oily stain, dark stain, and adhering hull will also remove 89.7% of nuts with kernel decay, 93.8% of nuts with Aspergillus molds present, and 98.7% of NOW positive nuts.