Ron P. Haff

Review: Recent Advances in the Use of Non-Destructive near Infrared Spectroscopy for Intact Olive Fruits:

The objective of this review is to illustrate the state of the art in the use of non-destructive near infrared (NIR) spectroscopy for quality evaluation of intact fruit in the olive industry. First, the most recent studies regarding the application of non-destructive NIR spectroscopy methods for the assessment of external olive quality are reviewed. External defects including mechanical damage, bruising, ground origin and insect infestation, and the consequences of these defects for finished products are reported. Second, research regarding chemical parameters of olive fruits is reviewed; in particular, the use of portable instruments to measure quality parameters such as moisture, oil and phenolic content while the fruit is on the tree, with the goal of monitoring the trends in these parameters during olive development. Finally, research on intact olive authenticity, an important aspect for legal and economic reasons, is reviewed. As most studies cited indicate the feasibility of NIR spectroscopy for non-destructive evaluation of many quality parameters, this review stresses the urgent need for technology transfer to olive facilities to enhance product quality while reducing production costs.

Image restoration of line-scanned x-ray images

Six specific defects in images captured on line-scan x-ray machines are discussed and software corrections are addressed. The corrections are intended to optimize the machine for the inspection of food products for hidden defects or contaminants. The defects include effects due to time constants in the preamplifier, decays (short and long term) of the phosphor in the detectors, gaps in the images at detector module boundaries, analog to digital converter (ADC) clipping of the photodiode signal, varying response between detectors to the low-energy end of the incident energy spectrum, and x-ray scattering. Of these, all but the long-term phosphor decay and scattering are corrected with software. While the long-term phosphor decay is of little consequence for food inspection, effects due to scattering are serious and must be addressed in the design of the detectors. The restored image is more suitable for image processing and thus for real-time sorting than the original image.

Detection of pits and pit fragments in fresh cherries using near infrared spectroscopy

Near infrared spectroscopy in the wavelength region from 800 to 2600 nm was evaluated as the basis for a rapid, nondestructive method for the detection of pits and pit fragments in fresh cherries. Partial least squares discriminant analysis following various spectral pretreatments was applied to spectra of cherries with either no pit, a whole pit, a half pit, or a quarter pit to test various classification schemes. An iterative algorithm tested all combinations of pretreatments and parameters as input to the partial least squares discriminant analysis. In addition, a step forward feature selection algorithm was used to identify the most significant wavebands in order to isolate small sets (<10) of spectral bands that represent the entire spectra. The highest accuracy was achieved for a binary model in which the samples were combined into only two classes (no pit versus whole pit + half pit + quarter pit) using all features (reflection at each wavelength) with no false positive error, 4% false negative error, and 98% overall accuracy. Overall accuracy of the same model was reduced only slightly to 96% when employing only the four most significant features. Accuracy declined when models attempted to separate the classes of fragments, with the lowest being 92, 83, 86, and 99% accuracy, respectively, in discriminating no pit, quarter pit, half pit, and whole pit classes separately. The high accuracy achieved under the binary model using only four features indicates that reflection of light at specific near infrared wavelengths is a suitable basis for high-speed, nondestructive detection of pits and pit fragments in cherries.

Methods for correcting morphological-based deficiencies in hyperspectral images of round objects

Hyperspectral images of curved surfaces contain undesirable artefacts that are a consequence of the morphology, or shape, of the sample. A software correction was developed to remove the variation in pixel intensity in hyperspectral images of spherical samples generated on a linescan type imaging system. The correction is based directly on well known physical effects involving light reflection and intensity. The three predominant principles investigated are the behaviour of light reflected from Lambertian surfaces, the 1/x2 relationship between light intensity and distance from the source, and the variation in arc length along a circle, as seen from the detectors. The algorithm was tested using hyperspectral images of a uniform spherical Teflon sample. Pixel intensity profiles and histograms were generated for the corrected images and evaluated to determine the effectiveness of the algorithm based on the fact that the ideal result would be a uniform image (as is appropriate for a uniform sample). Results indicate that the algorithm effectively improves pixel intensity uniformity, although some variability remains. Contributing factors to the remaining pixel intensity variation in the corrected images include non-uniformity of sample illumination, specular reflection, unintended ambient light and reflections from surfaces. The same principle can be applied to samples with circular cross sections along a particular axis, which includes many agricultural commodities.

Low Cost Real Time Sorting of In-shell Pistachio Nuts from Kernels

A high speed sorter for separating in-shell pistachio nuts from kernels is reported. Testing indicates 95% accuracy in removing kernels from the in-shell stream and 100% accuracy for the in-shells out of 1000 samples tested. Testing with 1000 each of in-shell, shell halves, and kernels resulted in an overall error of 3.3%, about twice the overall error rate achieved using a commercially available dual band VIS-NIR sorting device. The cost of materials for the sorter reported here was less than

Nondestructive detection of insect infested chestnuts based on NIR spectroscopy

500 US, compared to upward of

Feasibility of Vis/NIR spectroscopy for detection of flaws in hazelnut kernels

100,000 for commercially available sorters. The new sorter thus has approximately double the error rate while achieving a roughly two hundred fold savings in cost compared to currently used equipment. Since existing sorters can be trained to sort a variety of product streams, implementation of the new device in pistachio plants could free up machines for other sorting tasks, thus reducing the overall cost of pistachio processing.