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Foods ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 232
Author(s):  
Hanim Z. Amanah ◽  
Salma Sultana Tunny ◽  
Rudiati Evi Masithoh ◽  
Myoung-Gun Choung ◽  
Kyung-Hwan Kim ◽  
...  

The demand for rapid and nondestructive methods to determine chemical components in food and agricultural products is proliferating due to being beneficial for screening food quality. This research investigates the feasibility of Fourier transform near-infrared (FT-NIR) and Fourier transform infrared spectroscopy (FT-IR) to predict total as well as an individual type of isoflavones and oligosaccharides using intact soybean samples. A partial least square regression method was performed to develop models based on the spectral data of 310 soybean samples, which were synchronized to the reference values evaluated using a conventional assay. Furthermore, the obtained models were tested using soybean varieties not initially involved in the model construction. As a result, the best prediction models of FT-NIR were allowed to predict total isoflavones and oligosaccharides using intact seeds with acceptable performance (R2p: 0.80 and 0.72), which were slightly better than the model obtained based on FT-IR data (R2p: 0.73 and 0.70). The results also demonstrate the possibility of using FT-NIR to predict individual types of evaluated components, denoted by acceptable performance values of prediction model (R2p) of over 0.70. In addition, the result of the testing model proved the model’s performance by obtaining a similar R2 and error to the calibration model.


2022 ◽  
Author(s):  
Shomik Verma ◽  
Miguel Rivera ◽  
David O. Scanlon ◽  
Aron Walsh

Understanding the excited state properties of molecules provides insights into how they interact with light. These interactions can be exploited to design compounds for photochemical applications, including enhanced spectral conversion of light to increase the efficiency of photovoltaic cells. While chemical discovery is time- and resource-intensive experimentally, computational chemistry can be used to screen large-scale databases for molecules of interest in a procedure known as high-throughput virtual screening. The first step usually involves a high-speed but low-accuracy method to screen large numbers of molecules (potentially millions) so only the best candidates are evaluated with expensive methods. However, use of a coarse first-pass screening method can potentially result in high false positive or false negative rates. Therefore, this study uses machine learning to calibrate a high-throughput technique (xTB-sTDA) against a higher accuracy one (TD-DFT). Testing the calibration model shows a ~5-fold decrease in error in-domain and a ~3-fold decrease out-of-domain. The resulting mean absolute error of ~0.14 eV is in line with previous work in machine learning calibrations and out-performs previous work in linear calibration of xTB-sTDA. We then apply the calibration model to screen a 250k molecule database and map inaccuracies of xTB-sTDA in chemical space. We also show generalizability of the workflow by calibrating against a higher-level technique (CC2), yielding a similarly low error. Overall, this work demonstrates machine learning can be used to develop a both cheap and accurate method for large-scale excited state screening, enabling accelerated molecular discovery across a variety of disciplines.


2022 ◽  
Vol 14 (2) ◽  
pp. 319
Author(s):  
Tanzeel U. Rehman ◽  
Libo Zhang ◽  
Dongdong Ma ◽  
Jian Jin

Hyperspectral imaging has increasingly been used in high-throughput plant phenotyping systems. Rapid advancement in the field of phenotyping has resulted in a wide array of hyperspectral imaging systems. However, sharing the plant feature prediction models between different phenotyping facilities becomes challenging due to the differences in imaging environments and imaging sensors. Calibration transfer between imaging facilities is crucially important to cope with such changes. Spectral space adjustment methods including direct standardization (DS), its variants (PDS, DPDS) and spectral scale transformation (SST) require the standard samples to be imaged in different facilities. However, in real-world scenarios, imaging the standard samples is practically unattractive. Therefore, in this study, we presented three methods (TCA, c-PCA, and di-PLSR) to transfer the calibration models without requiring the standard samples. In order to compare the performance of proposed approaches, maize plants were imaged in two greenhouse-based HTPP systems using two pushbroom-style hyperspectral cameras covering the visible near-infrared range. We tested the proposed methods to transfer nitrogen content (N) and relative water content (RWC) calibration models. The results showed that prediction R2 increased by up to 14.50% and 42.20%, while the reduction in RMSEv was up to 74.49% and 76.72% for RWC and N, respectively. The di-PLSR achieved the best results for almost all the datasets included in this study, with TCA being second. The performance of c-PCA was not at par with the di-PLSR and TCA. Our results showed that the di-PLSR helped to recover the performance of RWC, and N models plummeted due to the differences originating from new imaging systems (sensor type, spectrograph, lens system, spatial resolution, spectral resolution, field of view, bit-depth, frame rate, and exposure time) or lighting conditions. The proposed approaches can alleviate the requirement of developing a new calibration model for a new phenotyping facility or to resort to the spectral space adjustment using the standard samples.


Molecules ◽  
2022 ◽  
Vol 27 (2) ◽  
pp. 335
Author(s):  
Ning Ai ◽  
Yibo Jiang ◽  
Sainab Omar ◽  
Jiawei Wang ◽  
Luyue Xia ◽  
...  

Near-infrared (NIR) spectroscopy and characteristic variables selection methods were used to develop a quick method for the determination of cellulose, hemicellulose, and lignin contents in Sargassum horneri. Calibration models for cellulose, hemicellulose, and lignin in Sargassum horneri were established using partial least square regression methods with full variables (full-PLSR). The PLSR calibration models were established by four characteristic variables selection methods, including interval partial least square (iPLS), competitive adaptive reweighted sampling (CARS), correlation coefficient (CC), and genetic algorithm (GA). The results showed that the performance of the four calibration models, namely iPLS-PLSR, CARS-PLSR, CC-PLSR, and GA-PLSR, was better than the full-PLSR calibration model. The iPLS method was best in the performance of the models. For iPLS-PLSR, the determination coefficient (R2), root mean square error (RMSE), and residual predictive deviation (RPD) of the prediction set were as follows: 0.8955, 0.8232%, and 3.0934 for cellulose, 0.8669, 0.4697%, and 2.7406 for hemicellulose, and 0.7307, 0.7533%, and 1.9272 for lignin, respectively. These findings indicate that the NIR calibration models can be used to predict cellulose, hemicellulose, and lignin contents in Sargassum horneri quickly and accurately.


Author(s):  
De-Qing Kong ◽  
Chunlai Li ◽  
Hongbo Zhang ◽  
Yan Su ◽  
Jian-Jun Liu ◽  
...  

Abstract The new Wuqing 70 m radio telescope is firstly used for the downlink data reception in the first Mars exploration mission of China, and will be used for the other deep space communications and radio astronomical observations in the future. The main specifications and measurement results of some properties in X-band are introduced in this paper, such as pointing calibration, gain and efficiency, system noise temperature, system equivalent flux density, and variations with elevation. The 23 parameters pointing calibration model considering the atmospheric refraction correction in real time is presented in the telescope, and the pointing accuracy is reached 5.70″ in azimuth direction and 6.07″ in elevation direction respectively for different weather condi-tions. More than 62% efficiencies are achieved at full elevation range, and more than 70% in the mid-elevation. The system equivalent flux density of X-band in the mid-elevation is reached 26 Jy.


2022 ◽  
Vol 8 ◽  
Author(s):  
Ruihao Li ◽  
Chunlian Fu ◽  
Wei Yi ◽  
Xiaodong Yi

The low-cost Inertial Measurement Unit (IMU) can provide orientation information and is widely used in our daily life. However, IMUs with bad calibration will provide inaccurate angular velocity and lead to rapid drift of integral orientation in a short time. In this paper, we present the Calib-Net which can achieve the accurate calibration of low-cost IMU via a simple deep convolutional neural network. Following a carefully designed mathematical calibration model, Calib-Net can output compensation components for gyroscope measurements dynamically. Dilation convolution is adopted in Calib-Net for spatio-temporal feature extraction of IMU measurements. We evaluate our proposed system on public datasets quantitively and qualitatively. The experimental results demonstrate that our Calib-Net achieves better calibration performance than other methods, what is more, and the estimated orientation with our Calib-Net is even comparable with the results from visual inertial odometry (VIO) systems.


Jurnal Agro ◽  
2022 ◽  
Vol 8 (2) ◽  
pp. 212-225
Author(s):  
Kusumiyati Kusumiyati ◽  
Ine Elisa Putri ◽  
Wawan Sutari ◽  
Jajang Sauman Hamdani

Cabe rawit umumnya berwarna hijau, jingga dan merah. Tiap tingkat kematangan memiliki kualitas yang berbeda. Teknologi non-destruktif visible/near infrared spectroscopy (Vis/NIRS) telah banyak digunakan untuk memprediksi kualitas secara cepat dan akurat serta tidak merusak. Penelitian bertujuan untuk mengetahui kandungan kadar air, total karotenoid dan antioksidan dua varietas buah cabai rawit dengan tingkat kematangan berbeda dan memprediksi kualitas secara non-destruktif menggunakan Vis/NIRS. Penelitian dilakukan di Laboratorium Hortikultura, Fakultas Pertanian, Universitas Padjadjaran. Penelitian disusun dalam rancangan acak lengkap (RAL) dengan 6 perlakuan yaitu varietas ‘Manik’ dan ‘Domba’, yang dipanen pada 20 hari setelah bunga mekar (HSBM), 40 HSBM dan 60 HSBM, serta diulang 5 kali. Data dianalisis dengan analisis varians (ANOVA). Hasil penelitian menunjukkan bahwa varietas ‘Manik’ and ‘Domba’ yang dipanen pada 20 HSBM memiliki kandungan kadar air dan antioksidan tertinggi sedangkan total karotenoid meningkat pada buah matang. Model kalibrasi dan uji validasi silang kadar air, total karotenoid, dan antioksidan mendapatkan nilai Rkal  ≥ 0,87 dan Rval  ≥ 0,84. Berdasarkan hasil tersebut, maka kandungan air dan antioksidan terbesar yaitu buah cabai rawit hijau sedangkan total karotenoid tertinggi pada buah cabai rawit merah. Vis/NIRS dapat digunakan untuk mendeteksi kandungan air, total karotenoid dan antioksidan pada buah cabai rawit. Generally, cayenne pepper is coloring in green, orange, and red. Each maturity level has a different quality.  A non-destructive technology, visible/near infrared spectroscopy (Vis/NIRS), has been widely used to predict the quality quickly and accurately without causing damage. The study aimed to determine water content, total carotenoids, and antioxidant of two varieties cayenne pepper with different maturity levels and to predict quality non-destructively using Vis/NIRS. The research was conducted at the Horticulture Laboratory, Agriculture Faculty, Universitas Padjadjaran. The research was arranged in a completely randomized design (CRD) with 6 treatments, namely ‘Manik’ and ‘Domba’ varieties harvested at 20 days after flowering (DAF), 40 DAF and 60 DAF, and 5 replications with analysis of variance (ANOVA). The results showed that ‘Manik’ and ‘Domba’ harvested at 20 DAF had the highest water content and antioxidant while the total carotenoids increased in ripe fruit. The calibration model and cross-validation of water content, total carotenoids, and antioxidants obtained values of Rcal 0.87 and Rval 0.84. Based on these results, the highest water and antioxidant content was green cayenne pepper, while the highest total carotenoids were in red cayenne pepper. Vis/NIRS can be used to detect water content, total carotenoids, and antioxidants in cayenne pepper.


Author(s):  
Ioane Muni Toke ◽  
Nakahiro Yoshida

AbstractThis paper extends the analysis of Muni Toke and Yoshida (2020) to the case of marked point processes. We consider multiple marked point processes with intensities defined by three multiplicative components, namely a common baseline intensity, a state-dependent component specific to each process, and a state-dependent component specific to each mark within each process. We show that for specific mark distributions, this model is a combination of the ratio models defined in Muni Toke and Yoshida (2020). We prove convergence results for the quasi-maximum and quasi-Bayesian likelihood estimators of this model and provide numerical illustrations of the asymptotic variances. We use these ratio processes to model transactions occurring in a limit order book. Model flexibility allows us to investigate both state-dependency (emphasizing the role of imbalance and spread as significant signals) and clustering. Calibration, model selection and prediction results are reported for high-frequency trading data on multiple stocks traded on Euronext Paris. We show that the marked ratio model outperforms other intensity-based methods (such as “pure” Hawkes-based methods) in predicting the sign and aggressiveness of market orders on financial markets.


MAUSAM ◽  
2021 ◽  
Vol 65 (1) ◽  
pp. 109-114
Author(s):  
NEERAJ KUMAR ◽  
SUMAN KUMAR ◽  
A.S. NAIN

The study aimed response of CERES-wheat and CROPGRO-urd model for tarai region of Uttarakhand. Field experiments were conducted at N. E. Borlaug, Crop Research Centre, G. B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand during rabi and kharif seasons 2007 and 2008. CERES-wheat and CROPGRO-urd models version v 4.5 were used in this study. Cultivar specific genotypic coefficients were derived for wheat and urd during calibration. Model validation based on several independent sets of growth and yield data, including different nitrogen and irrigation levels. For all parameters t-test was found non-significant (‘t’ calculated values were smaller than t tabulated values at 5% level of significance), indicating that there were least differences between observed and predicted values. The result obtained with the model demonstrated satisfactorily prediction of phenology, growth and yield and thus it can be used for the prediction of wheat and urd growth as well as yield in this region


Pharmaceutics ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2168
Author(s):  
Laurens Leys ◽  
Gust Nuytten ◽  
Joris Lammens ◽  
Pieter-Jan Van Bockstal ◽  
Jos Corver ◽  
...  

The pharmaceutical industry is progressing toward the development of more continuous manufacturing techniques. At the same time, the industry is striving toward more process understanding and improved process control, which requires the implementation of process analytical technology tools (PAT). For the purpose of drying biopharmaceuticals, a continuous spin freeze-drying technology for unit doses was developed, which is based on creating thin layers of product by spinning the solution during the freezing step. Drying is performed under vacuum using infrared heaters to provide energy for the sublimation process. This approach reduces drying times by more than 90% compared to conventional batch freeze-drying. In this work, a new methodology is presented using near-infrared (NIR) spectroscopy to study the desorption kinetics during the secondary drying step of the continuous spin freeze-drying process. An inline PLS-based NIR calibration model to predict the residual moisture content of a standard formulation (i.e., 10% sucrose) was constructed and validated. This model was then used to evaluate the effect of different process parameters on the desorption rate. Product temperature, which was controlled by a PID feedback mechanism of the IR heaters, had the highest positive impact on the drying rate during secondary drying. Using a higher cooling rate during spin freezing was found to significantly increase the desorption rate as well. A higher filling volume had a smaller negative effect on the drying rate while the chamber pressure during drying was found to have no significant effect in the range between 10 and 30 Pa.


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