Corn starch (CS) is one of the most common powders in industrial production and daily life, but its inherent risks of ignition and explosion cannot be disregarded. In this study, the flame propagation behaviors of CS at various concentrations were studied by a dust ignition experiment system. Meanwhile, the combustion process was analyzed in detail by differential scanning calorimetry tests, and the changes of key groups in molecules during the combustion process were monitored in real time by in-situ diffuse reflectance infrared Fourier transform spectroscopy tests. Based on this, the reaction process of CS combustion was analyzed. It was found that the dust concentration had a marked influence on the flame propagation characteristics. The oxidation process of CS underwent four stages in sequence: water evaporation stage, transition stage, rapid combustion stage, and coke carbonization stage. There were four key functional groups in the CS molecule: C-O-C, CH, CO, and -OH. 

Flame propagation characteristics and surface functional groups changes of corn starch dust during the combustion process

The effect of gas concentration on detection and classification of beef and pork mixtures using E-nose

Devices of human-based senses such as e-noses, e-tongues and e-eyes can be used to analyze different compounds in several food matrices. These sensors allow the detection of one or more compounds present in complex food samples, and the responses obtained can be used for several goals when different chemometric tools are applied. In this systematic review, we used Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, to address issues such as e-sensing with chemometric methods for food quality control (FQC). A total of 109 eligible articles were selected from PubMed, Scopus and Web of Science. Thus, we predicted that the association between e-sensing and chemometric tools is essential for FQC. Most studies have applied preliminary approaches like exploratory analysis, while the classification/regression methods have been less investigated. It is worth mentioning that non-linear methods based on artificial intelligence/machine learning, in most cases, had classification/regression performances superior to non-liner, although their applications were seen less often. Another approach that has generated promising results is the data fusion between e-sensing devices or in conjunction with other analytical techniques. Furthermore, some future trends in the application of miniaturized devices and nanoscale sensors are also discussed.

E-sensing and nanoscale-sensing devices associated with data processing algorithms applied to food quality control: a systematic review.

Effect of fat replacement by inulin on the physicochemical properties and sensory attributes of low-fat margarine

Seasonings play a key role in determining sensory attributes of instant starch noodles. Controlling and improving the quality of seasoning is becoming important. In this study, five different brands along with fifteen instant starch noodles seasonings (seasoning powder, seasoning mixture sauce and the mixture of powder and sauce) were characterized by electronic nose (e-nose) and electronic tongue (e-tongue). Feature-level fusion for the integration of the signals was introduced to integrate the e-nose and e-tongue signals, aiming at improving the performances of identification and prediction models. Principal component analysis (PCA) explained over 85.00% of the total variance in e-nose data and e-tongue data, discriminated all samples. Multilayer perceptron neural networks analysis (MLPN) modeling demonstrated that the identification rate of the combined data was basically 100%. PCA, cluster analysis (CA), and MLPN proved that the classification results acquired from the combined e-nose and e-tongue data were better than individual e-nose and e-tongue result. This work demonstrated that in combination e-nose and e-tongue provided more comprehensive information about the seasonings compared to each individual e-nose and e-tongue. E-nose and e-tongue technologies hold great potential in the production, quality control, and flavor detection of instant starch noodles seasonings.

Combining e-nose and e-tongue for improved recognition of instant starch noodles seasonings

The flavor profile of five brands of instant vermicelli seasonings were identified by headspace solid-phase micro-extraction coupled with gas chromatography-mass spectrometry (HS-SPME/GC-MS) and electronic nose (e-nose). GC-MS showed that the volatile compounds of instant vermicelli seasonings were significantly different. Alkenes, alcohols, aldehydes and ketones were the major volatile compounds in instant vermicelli seasonings. The seasonings could be classified based on differences in volatile compounds. The overall volatiles profiles were also analyzed by e-nose. E-nose determination and GC-MS statistical analysis had similar results. The volatile compounds showed good correlation with e-nose sensors according to partial least square regression models. Both methods had good potential application in evaluating flavor quality and differentiating among instant vermicelli seasonings.

Discrimination of five brands of instant vermicelli seasonings by HS-SPME/GC–MS and electronic nose

A probability model for predicting the transport efficiency in vertical pipes considering the particle size distribution

 This design takes STM32 single chip microcomputer as the core, and uses keil as the compiling environment of single chip microcomputer program in the software. The circuit schematic diagram of subway monitoring system is drawn on Altium designer.STM32 single chip microcomputer, infrared monitoring, temperature and humidity sensor, pressure sensor, LCD display, voice broadcasting system, alarm system Switches and other hardware are connected together by hand welding tools according to the schematic diagram and the functional requirements.

/pdf/the-design-and-implementation-of-a-subway-monitoring-system-ee6qweq1.pdf

The Design and Implementation of a Subway Monitoring System

Effect of Boron on Surface Tension of Liquid Fe-3.0%Si Alloys

Suppression mechanism of micron/nano PMMA dust flame based on thermal analysis

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