The visual perception of freshness is an important factor considered by consumers in the purchase of fruits and vegetables. However, panel testing when evaluating food products is time consuming and expensive. Herein, the ability of an image processing-based, nondestructive technique to classify spinach freshness was evaluated. Images of spinach leaves were taken using a smartphone camera after different storage periods. Twelve sensory panels ranked spinach freshness into one of four levels using these images. The rounded value of the average from all twelve panel evaluations was set as the true label. The spinach image was removed from the background, and then converted into a gray scale and CIE-Lab color space (L*a*b*) and Hue, Saturation and Value (HSV). The mean value, minimum value, and standard deviation of each component of color in spinach leaf were extracted as color features. Local features were extracted using the bag-of-words of key points from Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elementary Features). The feature combinations selected from the spinach images were used to train machine learning models to recognize freshness levels. Correlation analysis between the extracted features and the sensory evaluation score showed a positive correlation (0.5 < r < 0.6) for four color features, and a negative correlation (‒0.6 < r < ‒0.5) for six clusters in the local features. The support vector machine classifier and artificial neural network algorithm successfully classified spinach samples with overall accuracy 70% in four-class, 77% in three-class and 84% in two-class, which was similar to that of the individual panel evaluations. Our findings indicate that a model using support vector machine classifiers and artificial neural networks has the potential to replace freshness evaluations currently performed by non-trained panels.

The terrestrial slug Limax can learn to avoid the odor of some food (e.g., carrot juice) by the simultaneous presentation of an aversive stimulus (e.g., bitterness of quinidine). This type of associative memory critically depends on the higher olfactory center, the procerebrum in the central nervous system. The modulation of the local field potential (LFP) oscillation recorded on the procerebrum has been thought to reflect the information processing of the odor that elicits the behavioral change, such as avoidance of the aversively learned odor or approaching an attractive food's odor. Here we focused on octopamine, an important neuromodulator involved in learning and memory in invertebrates, and considered to be the invertebrate equivalent of noradrenaline. We identified a few octopaminergic neurons in the subesophageal and buccal ganglia, and a larger number near the procerebrum in the cerebral ganglia, using immunohistochmical staining and in situ hybridization of tyramine β-hydroxylase, an octopamine-synthesizing enzyme. Application of octopamine reduced the frequency of LFP oscillation in a dose-dependent manner, and this effect was inhibited by preincubation with phentolamine. High-performance liquid chromatography analysis revealed the presence of octopamine, noradrenaline, and adrenaline in the central nervous system. Unexpectedly, noradrenaline and adrenaline both accelerated the LFP oscillation, in contrast to octopamine. Our results suggest that octopamine and noradrenaline have distinct functions in olfactory information processing, in spite of their structural similarity. J. Comp. Neurol. 524:3849-3864, 2016. © 2016 Wiley Periodicals, Inc.