Oil source recognition technology using concentration-synchronous-matrix-fluorescence spectroscopy combined with 2D wavelet packet and probabilistic neural network

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology

As the raw material for tea making, the quality of tea leaves directly affects the quality of finished tea. The quality of fresh tea leaves is mainly assessed by manual judgment or physical and chemical testing of the content of internal components. Physical and chemical methods are more mature, and the test results are more accurate and objective, but traditional chemical methods for measuring the biochemical indexes of tea leaves are time-consuming, labor-costly, complicated, and destructive. With the rapid development of imaging and spectroscopic technology, spectroscopic technology as an emerging technology has been widely used in rapid non-destructive testing of the quality and safety of agricultural products. Due to the existence of spectral information with a low signal-to-noise ratio, high information redundancy, and strong autocorrelation, scholars have conducted a series of studies on spectral data preprocessing. The correlation between spectral data and target data is improved by smoothing noise reduction, correction, extraction of feature bands, and so on, to construct a stable, highly accurate estimation or discrimination model with strong generalization ability. There have been more research papers published on spectroscopic techniques to detect the quality of tea fresh leaves. This study summarizes the principles, analytical methods, and applications of Hyperspectral imaging (HSI) in the nondestructive testing of the quality and safety of fresh tea leaves for the purpose of tracking the latest research advances at home and abroad. At the same time, the principles and applications of other spectroscopic techniques including Near-infrared spectroscopy (NIRS), Mid-infrared spectroscopy (MIRS), Raman spectroscopy (RS), and other spectroscopic techniques for non-destructive testing of quality and safety of fresh tea leaves are also briefly introduced. Finally, in terms of technical obstacles and practical applications, the challenges and development trends of spectral analysis technology in the nondestructive assessment of tea leaf quality are examined.

Emerging Chemical Methods for Petroleum and Petroleum-Derived Dissolved Organic Matter Following the Deepwater Horizon Oil Spill

Despite the fact that oil chemistry and oils spills have been studied for many years, there are still emerging techniques and unknown processes to be explored. The 2010 Deepwater Horizon oil spill in the Gulf of Mexico resulted in a revival of oil spill research across a wide range of fields. These studies provided many new insights, but unanswered questions remain. Over 1,000 journal articles related to the Deepwater Horizon spill are indexed by the Chemical Abstract Service. Numerous ecological, human health, and organismal studies were published. Analytical tools applied to the spill include mass spectrometry, chromatography, and optical spectroscopy. Owing to the large scale of studies, this review focuses on three emerging areas that have been explored but remain underutilized in oil spill characterization: excitation-emission matrix spectroscopy, black carbon analysis, and trace metal analysis using inductively coupled plasma mass spectrometry.

Rapid fingerprinting technology of heavy oil spill by mid-infrared spectroscopy

With the increase of unconventional oil production and transportation, the detection methods of light crude oil have been challenged. Mid-Infrared spectroscopy can reflect the functional group of the oil related samples, which has strong absorption signals with distinguishable peaks featured as a fast, economy, and robust technique. Nevertheless, the previous study and application of oil relevant samples, such as petroleum chemical industry online monitoring, are mainly based on Near-infrared spectroscopy. Recently, the rapid development of the spectral instrument manufacturing and the data analysis methods provides a more comprehensive technical support for the rapid and accurate identification of marine oil spill by Mid-infrared spectroscopy. In this paper, 10 crude oil samples were selected for infrared spectroscopy detection, and the results were analysed and compared with those of gas chromatography flame ionization detection method. The character information of the IR spectra and GC/FID chromatograms were extracted and classified both by principal component analysis and partial least squares regression. Under the condition of small sample size, the recognition accuracy was up to 100%. The results show that the mid-infrared method combined with chemometrics can be expected to achieve rapid, accurate and economical identification of heavy oil species.

Enhanced Clean-In-Place Monitoring Using Ultraviolet Induced Fluorescence and Neural Networks

Clean-in-place (CIP) processes are extensively used to clean industrial equipment without the need for disassembly. In food manufacturing, cleaning can account for up to 70% of water use and is also a heavy user of energy and chemicals. Due to a current lack of real-time in-process monitoring, the non-optimal control of the cleaning process parameters and durations result in excessive resource consumption and periods of non-productivity. In this paper, an optical monitoring system is designed and realized to assess the amount of fouling material remaining in process tanks, and to predict the required cleaning time. An experimental campaign of CIP tests was carried out utilizing white chocolate as fouling medium. During the experiments, an image acquisition system endowed with a digital camera and ultraviolet light source was employed to collect digital images from the process tank. Diverse image segmentation techniques were considered to develop an image processing procedure with the aim of assessing the area of surface fouling and the fouling volume throughout the cleaning process. An intelligent decision-making support system utilizing nonlinear autoregressive models with exogenous inputs (NARX) Neural Network was configured, trained and tested to predict the cleaning time based on the image processing results. Results are discussed in terms of prediction accuracy and a comparative study on computation time against different image resolutions is reported. The potential benefits of the system for resource and time efficiency in food manufacturing are highlighted.

Cutting Pattern Identification for Coal Mining Shearer through a Swarm Intelligence-Based Variable Translation Wavelet Neural Network

As a sound signal has the advantages of non-contacted measurement, compact structure, and low power consumption, it has resulted in much attention in many fields. In this paper, the sound signal of the coal mining shearer is analyzed to realize the accurate online cutting pattern identification and guarantee the safety quality of the working face. The original acoustic signal is first collected through an industrial microphone and decomposed by adaptive ensemble empirical mode decomposition (EEMD). A 13-dimensional set composed by the normalized energy of each level is extracted as the feature vector in the next step. Then, a swarm intelligence optimization algorithm inspired by bat foraging behavior is applied to determine key parameters of the traditional variable translation wavelet neural network (VTWNN). Moreover, a disturbance coefficient is introduced into the basic bat algorithm (BA) to overcome the disadvantage of easily falling into local extremum and limited exploration ability. The VTWNN optimized by the modified BA (VTWNN-MBA) is used as the cutting pattern recognizer. Finally, a simulation example, with an accuracy of 95.25%, and a series of comparisons are conducted to prove the effectiveness and superiority of the proposed method.