Evaluation of freshness of lettuce using multi-spectroscopic sensing and machine learning

We aimed to develop a method to evaluate lettuce freshness changes during storage using only the surface color. In the first experiment, the surface color of one lettuce were measured continuously for 6 days. At the same time, moisture contents, elemental composition and organic matter of lettuce leaves were measured by oven drying method, X-ray fluorescent analysis and Mid-infrared spectroscopy, respectively. Considering a combination of the surface color and moisture and elemental contents, it was found that there were several color change points before and after the time when the moisture contents and elemental balances in the lettuce changed. These results represented that the surface color could relate to the internal quality. Additionally, it is suggested that freshness of lettuce could be quantified and predicted using surface color information.Furthermore, the data set and the method for freshness evaluation leading to machine learning were studied in the second experiment for the freshness judgement. In this experiment, 15 multispectral sensing data including lettuce color information were acquired, and the quality change point was determined using machine learning such as K-means and decision tree

Influences of pH and temperature on infrared spectroscopic features of brewed coffee

AbstractWe developed an infrared spectroscopic evaluation method of brewed coffee, whose quality and taste highly depend on the chemical contents, the interactions between the components, the pH value and the temperature, using a Fourier transform infrared (FT-IR) spectrometer equipped with an attenuated total reflection (ATR) accessory. The objective of this study is to understand the influences of the pH values and temperature on the spectral features of brewed coffee and the main components, since the component balances of the organic acids originating from coffee beans and being produced during processes such as roasting and extraction could closely relate to the brewed coffee characteristics. The absorption peak sifts of the ATR spectra of brewed coffee were observed as the influences of the pH and temperatures. Therefore, by analyzing the spectra of the coffee components under the various pH and temperature conditions based on the ionic dissociation equilibrium theory, the spectral behavior of the brewed coffee model due to the pH and temperature changes could mainly result from those of the organic acids as the main components. Consequently, the infrared spectral information analysis would be acceptable as a new method to evaluate a profile of brewed coffee for the quality evaluation relating to the taste and the non-intensive on-line monitoring of the coffee process

Effects of Assistance of High Frequency Dielectric and Infrared Heating on Vacuum Freeze Drying Characteristics of Food Model

[EN] By combining vacuum freeze drying combined with high-frequency dielectric and/or infrared heating, the drying time for frozen gels containing 1% agar with sucrose or sodium chloride was successfully shorten, and the drying time was influenced by the heating methods and by the additive component to the sample. Additionally, it was experimentally confirmed that the power consumption for freeze drying combined with electromagnetic wave heating could be reduced because of the shortened drying time. Consequently, this study could be a very important step for designing a vacuum freeze drying process optimally combining electromagnetic wave heating for each sample component.Hashimoto, A.; Suehara, K.; Kameoka, T.; Kawamura, K. (2018). Effects of Assistance of High Frequency Dielectric and Infrared Heating on Vacuum Freeze Drying Characteristics of Food Model. En IDS 2018. 21st International Drying Symposium Proceedings. Editorial Universitat Politècnica de València. 795-802. https://doi.org/10.4995/IDS2018.2018.7461OCS79580

Development of a Simultaneous Quantification Method for Multiple Modes of Nitrogen in Leaf Models Using Near-Infrared Spectroscopic Measurement

By focusing our attention on nitrogen components in plants, which are important for cultivation management in data-driven agriculture, we developed a simple, rapid, non-chemical and simultaneous quantification method for proteinic and nitrate nitrogen in a leaf model based on near-infrared (NIR) spectroscopic information obtained using a compact Fourier Transform NIR (FT-NIR) spectrometer. The NIR spectra of wet leaf models impregnated with a protein–nitric acid mixed solution and a dry leaf model obtained by drying filter paper were acquired. For spectral acquisition, a compact MEMS (Micro Electro Mechanical Systems) FT-NIR spectrometer equipped with a diffuse reflectance probe accessory was used. Partial least square regression analysis was performed using the spectral information of the extracted absorption bands based on the determination coefficients between the spectral absorption intensities and the contents of the two-dimensional spectral analysis between NIR and mid-infrared spectral information. Proteinic nitrogen content in the dry leaf model was well predicted using the MEMS FT-NIR spectroscopic method. Additionally, nitrate nitrogen in the dry leaf model was also determined by the provided method, but the necessity of adding the data for a wider range of nitric acid concentrations was experimentally indicated for the prediction of nitrate nitrogen content in the wet leaf model. Consequently, these results experimentally suggest the possibility of the application of the compact MEMS FT-NIR for obtaining the bioinformation of crops at agricultural on-sites