Evaluation of Camouflage Coloration of Polyamide-6,6 Fabric by Comparing Simultaneous Spectrum in Visible and Near-Infrared Region for Defense Applications

Polyamide-6,6 (PA-6,6) knitted fabric was coated with a complex combination of liquid-phase oxidized carbon black pigment (CBP) as light absorber and mono-sulfonated telon violet 3R (TVR) as acid dyes. Nitric acid (NA) moiety was used as liquid-phase oxidation of CBP and hydrophilic transformation of CBP-TVR. Thermoplastic polyurethane (TPU) and N, N-dimethylformamide (DMF) were formulated as cross-linker between composite mixture (CM) and PA-6,6 fabric. Six different CMs were coded for coating of PA-6, 6 fabric such as TPU-DMF, CBP-TPU-DMF, TVR-TPU-DMF, CBP-TVR-TPU-DMF, NA-TVR-TPU-DMF, NA-CBP-TVR-TPU-DMF. Structural, chromatic, and spectral reflection of CM coated PA-6,6 fabric was investigated by scanning electron microscopy, color measurement spectrophotometer, and Fourier transform infrared spectroscopy. CBP formulated PA-6,6 fabric was significantly remarked as maximum light absorber in both visible and near-infrared spectrum without allowing other parameters of treated PA-6,6 fabric. Therefore, minimum light reflection principle of CBP was indicated as camouflage material for camouflage textile coloration/finishing/patterning of simultaneous spectrum probe in visible and near-infrared spectrum. PA-6,6 fabric is very common fabrication for defense clothing, weapon, and vehicle netting against every combat background. This approach of simultaneous spectrum probe may be extended for concealment of target signature against high-performance defense surveillance.

Agricultural soil fertility properties in the near infrared spectrum

Soil spectrum in the near infrared (NIR) wavelength region can be used to reveal fertility properties which is related to plant cultivations. The main purpose of this presented paper is to study the soil spectrum in the NIR region and its related to the fertility properties in form of heavy metals like Fe and Cu. Soil samples were obtain from several land-use including agriculture, mining and ground field. Near infrared spectrum of soil samples were acquired in wavelength range from 1000 to 2500 nm. Prediction models used to determine Fe and Cu were built by means of partial least squares regression (PLSR) followed by leverage cross validation. Prediction performance was evaluated using coefficient of determination (r2) and ratio of prediction to deviation (RPD). The results showed that both Fe and Cu can be revealed simultaneously using the NIR spectrum with maximum r2 and RPD indexes were 0.93 and 3.86 for Fe and 0.71 and 1.88 for Cu prediction respectively. Based on the achieved results, it may conclude that soil fertility properties can be revealed simultaneously and rapidly using mear infrared spectral data.

Research on TFe Content of Hematite Based on LU-TELM-SOA and Selection of Band

Iron ore is an important raw material for the steel industry, so it is of great economic significance to determine the grade of the iron ore quickly and accurately. And the TFe content is the main indicator that determines the grade of the iron ore and whether the iron ore can be smelted directly. Unlike manual methods and methods for chemical analysis, the paper uses the selection of band for the near-infrared spectrum based on the pruning method and the two-hidden-layer extreme learning machine based on LU decomposition and seagull optimization algorithm (LU-TELM-SOA) to identify the TFe content. First of all, the paper proposes the selection of band based on the pruning method to retain the sensitive band of the near-infrared spectrum. Aiming at the problems of poor stability and low accuracy of a single LU-TELM (the two-hidden-layer extreme learning machine based on LU decomposition) model, the paper proposes LU-TELM-SOA. The experimental results show that LU-TELM-SOA has the advantages of high accuracy and strong stability.

Theoretical Simulation of Near-Infrared Spectrum of Piperine: Insight into Band Origins and the Features of Regression Models

We investigated the near-infrared spectrum of piperine using quantum mechanical calculations. We evaluated two efficient approaches, DVPT2//PM6 and DVPT2//ONIOM [PM6:B3LYP/6-311++G(2df, 2pd)] that yielded a simulated spectrum with varying accuracy versus computing time factor. We performed vibrational assignments and unveiled complex nature of the near-infrared spectrum of piperine, resulting from a high level of band convolution. The most meaningful contribution to the near-infrared absorption of piperine results from binary combination bands. With the available detailed near-infrared assignment of piperine, we interpreted the properties of partial least square regression models constructed in our earlier study to describe the piperine content in black pepper samples. Two models were compared with spectral data sets obtained with a benchtop and a miniaturized spectrometer. The two spectrometers implement distinct technology which leads to a profound instrumental difference and discrepancy in the predictive performance when analyzing piperine content. We concluded that the sensitivity of the two instruments to certain types of piperine vibrations is different and that the benchtop spectrometer unveiled higher selectivity. Such difference in obtaining chemical information from a sample can be one of the reasons why the benchtop spectrometer performs better in analyzing the piperine content of black pepper. This evidenced direct correspondence between the features critical for applied near-infrared spectroscopic routine and the underlying vibrational properties of the analyzed constituent in a complex sample.