Research on method for high sensitive detection of harmful gases in livestock houses based on laser absorption spectrum

Harmful gases such as ammonia and hydrogen sulfide in livestock and poultry houses can seriously damage the health of livestock and poultry as well as animal keepers, so it is great significant to detect these harmful gases rapidly and accurately for the improvement of the welfare of animals and the health of animal keepers. Laser absorption spectroscopy is a gas detection method with the advantages of high sensitivity and selectivity, and is widely used in industrial gas detection. However, it needs further exploring to verify whether laser absorption spectroscopy is useful in detecting low concentration harmful gases in livestock and poultry houses. This paper researches on the method for high-sensitivity detection of harmful gases in livestock and poultry houses based on laser absorption spectroscopy by detecting the absorption signals of ammonia with a self-designed system including a tunable laser wavelength scanning system, a photoelectric detecting system and a long light path gas absorption well, and verifies that laser absorption spectroscopy can be used for detecting harmful gases in livestock and poultry houses

Infrared microspectroscopy and machine learning: A novel approach to determine the origin and variety of individual rice grains

Accurately distinguishing the origin and variety of rice types is of paramount importance to conducting research on this staple crop. While various methods are currently employed for this purpose, few approaches can verify the identity of single grains rapidly and accurately. In this study, we present a method that integrates machine learning with infrared (IR) microspectroscopy for swift detection of the origin and variety of a single rice grain. To establish the validity of our approach, we assembled a diverse collection of rice samples, comprising 14 distinct types with different origins or varieties. Each rice sample yielded 100 microspectroscopy spectra, resulting in 1400 spectra. We applied two deep learning algorithms, deep neural network (DNN) and convolutional neural network (CNN), for spectral analysis. The 1400 spectra were randomly partitioned into calibration and validation sets at a ratio of 3:1. These datasets were subjected to both DNN and CNN analysis for classification of samples by origin and variety. Following 10,000 iterations, we selected optimal DNN and CNN models. The predication accuracies of the optimal DNN model for calibration and validation sets were 95.4% and 90.0%, respectively. In comparison, the optimal CNN model demonstrated superior accuracy, with 99.8% for the calibration set and 92.0% for the validation set. Based on these results, we selected the CNN model as the final model for field use in rice grain classification

Gas Imaging with Uncooled Thermal Imager

Gas imaging has become one of the research hotspots in the field of gas detection due to its significant advantages, such as high efficiency, large range, and dynamic visualization. It is widely used in industries such as natural gas transportation, chemical, and electric power industries. With the development of infrared detector technology, uncooled thermal imagers are undergoing a developmental stage of technological advancement and widespread application. This article introduces a gas imaging principle and radiation transfer model, focusing on passive imaging technology and active imaging technology. Combined with the actual analysis, the application scenarios using uncooled thermal imaging cameras for gas imaging measurement are analyzed. Finally, the limitations and challenges of the development of gas imaging technology are analyzed

Rapid Determination of Different Ripening Stages of Occidental Pears (<i>Pyrus communis</i> L.) by Volatile Organic Compounds Using Proton-Transfer-Reaction Mass Spectrometry (PTR-MS)

Determination of Occidental pear (Pyrus communis) ripening is difficult because the appearance of Occidental pears does not change significantly during the ripening process. Occidental pears at different ripening stages release different volatile organic compounds (VOCs), which can be used to determine fruit ripeness non-destructively and rapidly. In this study, VOCs were detected using proton-transfer-reaction mass spectrometry (PTR-MS). Notably, data were acquired within 1 min. Occidental pears harvested at five separate times were divided into three ripening stages: unripe, ripe, and overripe. The results showed that the composition of VOCs differed depending on the ripening stage. In particular, the concentrations of esters and terpenes significantly increased during the overripe stage. Three ripening stages were clearly discriminated by heatmap clustering and principal component analysis (PCA). This study provided a rapid and non-destructive method to evaluate the ripening stages of Occidental pears. The result can help fruit farmers to decide the optimum harvest time and hence reduce their economic losses

Potential using of infrared thermal imaging to detect volatile compounds released from decayed grapes

<div><p>Previous studies have demonstrated variations in volatile compound content during fruit spoilage. Infrared spectroscopy was proposed as an alternative method to discriminate the various states of decayed fruit through the makeup of their volatile compounds. Based on the infrared spectra of volatile compounds obtained from decayed grapes, this study simplified the extraction of their feature spectra and visualized their gas plumes by using a commercial infrared thermal camera equipped with a custom-made wavelength filter. As a function of volatilization gradients, accumulated gray value and imaging area were proposed as indicators for semi-quantitative analysis in a volatilization range similar to that of ethanol solutions ranging from 10% to 70%. Fresh, seriously decayed, and slightly or moderately decayed grapes were rapidly discriminated through their alcoholic volatiles by thermal images with correct classification ratings of 100%, 93.3%, and 90%, respectively.</p></div

Grape samples at different spoilage stages.

<p>(A) Fresh, (B) slightly decayed, (C) moderately decayed, and (D) seriously decayed.</p

A Review on Optical Measurement Method of Chemical Oxygen Demand in Water Bodies

International audienceWater quality monitoring technology based on optical method is the trend for modern water environmental monitoring. Compared with the traditional monitoring methods, Spectroscopy is a more simple, a small amount of reagent consumption, good repeatability, high accuracy and rapid detection of significant advantages, which is very suitable for rapid and on-line monitoring determination of environment water samples COD. This paper summarized the status and research progress of optical methods for monitoring of COD in water. The basic principle of traditional analysis methods and optical methods for measuring COD in water were brief described, and compared to the characteristic of different waveband of the detection of COD. The principles and applications of spectroscopic methods commonly used spectral preprocessing methods and calibration methods were listed, and also introduced the progress of optical sensors. Finally, the future research focus and direction of spectroscopic methods were prospected

Clustering of grapes during spoilage.

<p>Grapes were classified into three clusters through Euclidean distance by taking the logarithm of AGV and AIA in the thermal images of volatile compounds released from different spoilage stages of grapes.</p

Images of vapors released from ethanol solutions of different concentrations.

<p>An example of false color images of visualized gas plumes when the ethanol concentration ranges from 90% to 10% (from A to I).</p

Schematic diagram of the experimental set-up and the wavelength band chosen for the filter.

<p>(A) Infrared feature spectra of alcohols released from decayed grapes, as recorded in our previous study. The marked area shows the wavelength band chosen in this study (redrawn from Dong et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0180649#pone.0180649.ref010" target="_blank">10</a>]). (B) Non-dimensional diagram of the experimental set-up.</p