Detection of Oil-Containing Dressing on Salad Leaves Using Multispectral Imaging

Abstract

There is a need for non-invasive methods for nutritional analysis of food that can address the drawbacks of current methods such as color photography, which cannot distinguish between energydense and zero calorie foods. This paper discusses a novel, multispectral approach for the identification of oil, particularly in salads, and defines a pseudo-reflectance term. A custom-made multispectral camera was used to collect a novel, publicly shared dataset of images of untreated lettuce leaves or leaves treated with vinegar, oil, or a combination of these. The camera captured image data at 10 wavelengths ∈[380nm,980nm] across the electromagnetic spectrum in the visible and NIR (near-infrared) regions. Imaging was done in a lab environment with the presence of ambient light. Mean spectra were extracted from the regions of interest in the multispectral cube and used to compute pseudo-reflectance. ANOVA (Analysis of Variance) was performed to look for variances in the pseudo-reflectance curves. ANOVA proved that the differences between group means of the four treatment groups (oil, vinegar, oil plus vinegar, control) were statistically significant. Pairs of groups showing the greatest significance were established using a Tukey post hoc test. Sequential Forward Selection (SFS) was used to determine 5 optimal feature wavelengths from the whole feature space (410 nm, 455 nm, 485 nm, 810 nm, and 850 nm). A combination of visible (VIS) and infrared (IR) wavelengths, selected using SFS, showed the greatest potential for discrimination between groups containing oil and groups that do not contain oil with a classification accuracy of 84.20%. The pseudo-reflectance values were statistically proven to be sensitive to the presence of oil as a dressing. This research has demonstrated the feasibility of implementing a multispectral imaging technique for identifying the presence of oil in salads and possibly an energy content detection system. INDEX TERMS Multispectral imaging, energy density, food detection, high calorie detection, dietary assessment.

Transmittance Multispectral Imaging for Reheated Coconut Oil Differentiation

“…In addition, SI has been used for crop monitoring, and vegetation index calculation [59] and to assess the health status of crops [60]. In [61], an MIS has been developed to detect oil-containing dressing on salad leaves to assess the energy intake. Furthermore, SI has been used to analyze coconut oil adulteration with palm oil [62], mustard oil [63].…”

“…For the first study in III-D2, labelled sets are required to train the classifier with the reheat cycle count as the class label. According to related work on using multispectral images to detect oil-containing salad dressing [61], a sample size of 44.6 is sufficient for each class to produce a statistically significant result according to [83]. Hence, sixty labelled sets were prepared from the 8100 spectral signatures recorded for each reheat cycle count class.…”

Transmittance Multispectral Imaging for Reheated Coconut Oil Differentiation

“…In addition, SI has been used for crop monitoring and vegetation index calculation [51] and to assess the health status of crops [52]. In [53], an MIS has been developed to detect oil-containing dressing on salad leaves to assess the energy intake. Furthermore, SI has been used to analyze coconut oil adulteration with palm oil [54], mustard oil [55], etc.…”

Optimized Method Based on Subspace Merging for Spectral Reflectance Recovery

“…Spectral reflectance is an inherent nature of matter itself, which can be regarded as the ‘fingerprint’ of an object [ 1 ]. Due to its unique properties, it can be used to characterize the color of materials and the surface properties of materials in art [ 2 , 3 , 4 ], remote sensing [ 5 , 6 ], medicine [ 7 , 8 , 9 ], textiles [ 7 , 10 ] and so on [ 11 , 12 ]. In real life, it is difficult to directly obtain the surface spectral reflectance of an object; researchers mostly obtain the surface spectral reflectance by indirectly obtaining the response value of a digital camera [ 9 ].…”