Root Mean Standard Error Research Articles

Wavelength selection enables robust quantification of oil content with near-infrared spectroscopy in pea protein gels produced under varying heating conditions

Thermal processing influences the near-infrared (NIR) spectra of food products, causing inaccuracy when quantifying the composition of the product. In this study, the effect of thermal processing on NIR measurements of the oil content in pea protein isolate (PPI) gels was investigated. Analysis of variance showed that the heating temperature (30°C–120 °C) during the production of PPI gels influenced large parts of the NIR spectra and time (2.5–15 min) only had a limited effect. Stepwise multiple linear regression was used to select a combination of 5 wavelengths that was suitable to create a robust model for the prediction of oil content (Q2 = 0.9928 ± 0.002, root mean standard error of prediction = 0.2806 ± 0.0423 wt%). This combination of wavelengths was shown to reduce the effect of the history of thermal processing on the measurement, improving the accuracy of oil content quantification. This approach can be applied for in-line quantification of the oil content of food products subjected to varying heating conditions using specialized sensors.

Modelling global mesozooplankton biomass using machine learning

Mesozooplankton are a crucial link between primary producers and higher trophic levels and play a vital role in marine food webs, biological carbon pumps, and sustaining fishery resources. However, the global distribution of mesozooplankton biomass and the relevant controlling mechanisms remain elusive. We compared four machine learning algorithms (Boosted Regression Trees, Random Forest, Artificial Neural Network, and Support Vector Machine) to model the spatiotemporal distributions of global mesozooplankton biomass. These algorithms were trained on a compiled dataset of published mesozooplankton biomass observations with corresponding environmental predictors from contemporaneous satellite observations (temperature, chlorophyll, salinity, and mixed layer depth). We found that Random Forest achieved the best predictive accuracy with R2 and RMSE (Root Mean Standard Error) of 0.57 and 0.39, respectively. Also, the global distribution of mesozooplankton biomass predicted by the Random Forest model was more consistent with the observational data than other models. We used the Random Forest model to create a global map of mesozooplankton biomass which serves as a reference for validating process-based ecosystem models. The model outputs confirm that environmental factors, especially surface Chl a, a proxy for prey availability, significantly correlate with the spatiotemporal distribution of mesozooplankton biomass. The scaling relationship between the mesozooplankton biomass and Chl a can be used as an emergent constraint for model validation and development. Moreover, our model predicts that the global mesozooplankton biomass will decrease by 3% by the end of this century under the “business-as-usual” scenarios, potentially reducing fishery production and carbon sequestration. Our study contributes to predicting global mesozooplankton biomass and provides deep insights into the underlying environmental impacts on the distribution of mesozooplankton biomass.

ANALYSIS OF THE CALCULATION OF REFERENCE EVAPOTRANSPIRATION ACCORDING TO THE DATA OF THE STATE METEOROLOGICAL STATION

Since direct measurement of reference evapotranspiration (ET0) is a complex, time-consuming and expensive process, the most common procedure is to estimate ET0 from climate data. The purpose of this study was to perform reference evapotranspiration calculations based on the data of the state meteorological station Askania-Nova and compare them with the actual ET0 data obtained using an automatic Internet meteorological station. The data for the study were taken from the state meteorological station Askania-Nova (township Askania-Nova, Kakhovsky district, Kherson region, 46.45°N 33.88°E) and the automatic Internet meteorological station iMetos IMT 300 from the company "Pessl Instruments", which is located at the meteorological site of the Askaniysk DSDS (Tavrychanka village, Kakhovsky district, Kherson region, 46.55°N, 33.83°E). Standard evapotranspiration was calculated using the Penman-Monteith method (FAO56-RM). To assess the accuracy of ET0 calculations, mean absolute percent error (MAPE), root mean square error (RMSE) and Standard Error of Estimate (SEE ) were determined. According to the results of the comparison of indicators from two meteorological stations, it was found that the smallest errors are inherent in the daily average and maximum temperature and relative air humidity (MAPE<10%), for the minimum temperature and relative air humidity, the MAPE errors are 18,1 and 13,7%, respectively. The MAPE error for water vapor pressure deficit and solar radiation is 20,2 and 26,3%, respectively. The largest MAPE error of 40,3% was established for wind speed measurements. The average MAPE error between the calculated ET0, based on the meteorological data of the Askania-Nova station, and the actual ET0 data obtained from the automatic Internet meteorological station iMetos is 16,8%, RMSE – 0,65 mm, SEE – 0,56 mm. Applying a coefficient of 0,92 when calculating ET0 reduces the errors of MAPE, RMSE, and SEE by 3,2%, 0,15 mm, and 0,05 mm, respectively, for all calculation periods. For the May-August period, the MAPE error was 10,7%, which brings the calculations close to high accuracy (MAPE <10%). Based on the results of the calculations, it was established that on average over the years of research, the actual ET0 was 68 mm less than the calculated one. The absolute errors of determination of ETc depended on the crop and the average over the years of research ranged from 33 mm (winter wheat) to 68 mm (early tomatoes). The application of the refined value of ET0 in calculations reduces the absolute errors in the determination of c over the years of research, this error did not exceed 6 mm (early tomato). Research results confirm the possibility of using meteorological indicators obtained from state meteorological stations to calculate ET0. To increase the accuracy of calculations, it is necessary to use a refinement coefficient.

Application of in-line Raman spectroscopy to monitor crystallization and melting processes in milk fat

Anhydrous milk fat (AMF) and its fractions are used as ingredients in a wide range of food applications. Obtaining the appropriate solid fat content (SFC) is essential to achieve the desired product texture. At present, in-line monitoring techniques to control milk fat crystallization and melting are largely unavailable. The thermal behaviour of milk fat (AMF and four of its fractions) was monitored in a temperature-controlled vessel using an in-line Raman analyser and compared with thermograms generated using differential scanning calorimetry (DSC). The major stages of milk fat crystallization and melting were identified using the in-line Raman analyser. Thermal data from DSC showed excellent linear correlations with Raman spectral data (R2 value of 0.97 for the onset of milk fat crystallisation). Partial least squares regression (PLSR) models were developed using Raman spectra to predict SFC with coefficient of determination (R2Cs) from 0.929 to 0.992 and root mean standard error of calibration (RMSECs) ranging from 3.20 to 10.36%. Results demonstrated Raman spectroscopy has significant potential as a way of monitoring milk fat crystallization and melting processes.

Prediction of soil organic carbon stock combining Sentinel-1 and Sentinel-2 images in the Zoige Plateau, the northeastern Qinghai-Tibet Plateau

Abstract Background Soil organic carbon (SOC) is a critical component of the global carbon cycle, and an accurate estimate of regional SOC stock (SOCS) would significantly improve our understanding of SOC sequestration and cycles. Zoige Plateau, locating in the northeastern Qinghai-Tibet Plateau, has the largest alpine marsh wetland worldwide and exhibits a high sensitivity to climate fluctuations. Despite an increasing use of optical remote sensing in predicting regional SOCS, optical remote sensing has obvious limitations in the Zoige Plateau due to highly cloudy weather, and knowledge of on the spatial patterns of SOCS is limited. Therefore, in the current study, the spatial distributions of SOCS within 100 cm were predicted using an XGBoost model—a machine learning approach, by integrating Sentinel-1, Sentinel-2 and field observations in the Zoige Plateau. Results The results showed that SOC content exhibited vertical distribution patterns within 100 cm, with the highest SOC content in topsoil. The tenfold cross-validation approach showed that XGBoost model satisfactorily predicted the spatial patterns of SOCS with a model efficiency of 0.59 and a root mean standard error of 95.2 Mg ha−1. Predicted SOCS showed a distinct spatial heterogeneity in the Zoige Plateau, with an average of 355.7 ± 123.1 Mg ha−1 within 100 cm and totaled 0.27 × 109 Mg carbon. Conclusions High SOC content in topsoil highlights the high risks of significant carbon loss from topsoil due to human activities in the Zoige Plateau. Combining Sentinel-1 and Sentinel-2 satisfactorily predicted SOCS using the XGBoost model, which demonstrates the importance of selecting modeling approaches and satellite images to improve efficiency in predicting SOCS distribution at a fine spatial resolution of 10 m. Furthermore, the study emphasizes the potential of radar (Sentinel-1) in developing SOCS mapping, with the newly developed fine-resolution mapping having important applications in land management, ecological restoration, and protection efforts in the Zoige Plateau.

The Olivine-Spinel-aSiO2melt (OSaS) Oxybarometer: A New Method for Evaluating Magmatic Oxygen Fugacity in Olivine-Phyric Basalts

Abstract The compositions of cotectic olivine-spinel pairs in mafic magmas provide information on the oxygen fugacity of their host liquid, which can be accessed with a thermodynamic analysis of the olivine-spinel-liquid peritectic reaction: 3 F e 2 S i O 4 o l i v + O 2 s y s = 2 F e 3 O 4 s p + 3 S i O 2 m e l t The extraction of redox information from cotectic olivine-spinel pairs requires a well-defined silica activity value (aSiO2melt) for the melt of interest, as well as a method to calculate aFe2SiO4oliv and aFe3O4sp from chemical analyses of olivine and spinel, respectively. In this work, we develop a new olivine-spinel-aSiO2melt (OSaS) oxygen barometer that utilizes MELTS to obtain values of aSiO2melt, which are used with values aFe2SiO4oliv and aFe3O4sp determined from established solution models for olivine and spinel. We find that two implementations of the spinel-liquid peritectic equilibria can successfully generate magmatic oxygen fugacity values, (1) using a combination of mineral activity models from the literature (classical-OSaS) and aSiO2melt determined from MELTS, and (2) directly from chemical potentials obtained from MELTS, where a correction is added to the MELTS-derived chemical potentials for the magnetite component of the spinel (MELTS-OSaS). The two implementations of the OSaS were tested by using each model to recover the experimentally reported fO2 values for a dataset consisting of 50 olivine-spinel-glass assemblages derived from 14 published experimental studies. This dataset was filtered to remove potential disequilibrium phase assemblages, experiments with failed redox buffers, and poor-quality EMP analyses. Data quality metrics for the dataset filtration included Fe-Mg partitioning between olivine and melt, Fe-Mg partitioning between olivine-spinel, and an examination of whether aSiO2melt values were consistent with the reported phase assemblages. The classical-OSaS implementations reproduced the fO2 values reported from the experimental dataset with a standard error estimate (SEE) of ±0.39, root mean standard error (RMSE) of ±0.40 and average deviation of ±0.31. In testing the MELTS-OSaS model, we identified that the solution model for magnetite underpredicted the values of aFe3O4sp; therefore, we used the 50 experiments to assign a correction to the MELTS-predicted chemical potentials of magnetite. We tested 35 the MELTS-OSaS model with the magnetite correction on a dataset of 18 additional buffered experiments, filtered for redox equilibrium and not included in the original experimental dataset. We find that the MELTs-OSaS model, which includes the correction for the magnetite chemical potential, reproduces fO2 values for the 18 experiments with an SEE of ±0.20, root mean standard error (RMSE) of ±0.23 and average deviation of ±0.18. The OSaS oxybarometer can return magmatic fO2 values with a standard error of ± 0.20 to 0.39 log units, depending on the model selected, provided that the olivine-spinel cotectic temperature is known to an accuracy of ±25°C, the H2O content of the melt can be estimated within ±1.5wt%, and that the crystallization pressure of the olivine-spinel pair is < 500 MPa. We also propose that the OSaS models can be applied to experimental run products to determine or confirm oxygen fugacity values. We additionally suggest that the careful application of the OSaS oxybarometer can provide a reliable and robust alternative for performing redox studies on samples that do not contain sufficient glassy material to support the application of spectroscopic techniques (i.e., XANES and Mössbauer).

Near-infrared spectroscopy to quantify overall thermal process intensity during high-moisture extrusion of soy protein concentrate

High-moisture extrusion (HME) is widely used to produce meat analogues. During HME the plant-based materials experience thermal and mechanical stresses. It is complicated to separate their effects on the final products because these effects are interrelated. In this study we hypothesize that the intensity of the thermal treatment can explain a large part of the physicochemical changes that occur during extrusion. For this reason, near–infrared (NIR) spectroscopy was used as a novel method to quantify the thermal process intensity during HME. High–temperature shear cell (HTSC) processing was used to create a partial least squares (PLS) regression curve for processing temperature under controlled processing conditions (root mean standard error of cross-validation (RMSECV) = 4.00 °C, coefficient of determination of cross-validation (R2CV) = 0.97). This PLS regression model was then applied to HME extrudates produced at different screw speeds (200–1200 rpm) and barrel temperatures (100–160 °C) with two different screw profiles to calculate the equivalent shear cell temperature as a measure for thermal process intensity. This equivalent shear cell temperature reflects the effects of changes in local temperature conditions, residence time and thermal stresses. Furthermore, it can be related to the degree of texturization of the extrudates. This information can be used to gain new insights into the effect of various process parameters during HME on the thermal process intensity and extrudate quality.

Variability of time duration analysis for rainfall water using precipitation indexes in Hai town

Abstract Insufficient rainfall has an impact on a variety of natural resources. This work aims to determine the variability of rainfall and drought in Hai town depending on the standardized rainfall index (SRI), rainfall concentration index (RCI), index of wetness (IW), and coefficient of variation (CV). Rainfall series were taken from the Meteorological Station Directorate of Hai Town, Iraq for a period of 30 years (1989–2018). The results indicated that the years 1996 and 2014 had high SRI and were under extremely wet conditions (IW = 195.93 and 165.93, respectively). However, the lowest SRI value was in 2004, with a wetness index of 35.15, whereas the RCI was strongly irregular in rainfall distribution. Also, the CV was highly variable that ranged between 113.78 and 244.01. Mathematical models were created and confirmed for predicting the wetness index using data-fitting software. Model 1 generated best outcomes (R2 = 99.99%, relative error (RE) = 0.221, root mean square error (RMSE = 0.253) and standard error of estimates (SEE = 0.28). The results demonstrated that rain indicators have significant differences and alteration throughout the study period. Hence, the best model for estimating wetness and droughts in Hai town is recommended.

Design, fabrication and evaluation of a robot for plant nutrient monitoring in greenhouse (case study: Iron nutrient in spinach)

The regulation of nutrients intake in plant life is a critical factor in preserving the quality of crops, enhancing yield rates, and minimizing the fertilizer consumption. Consequently, this research presents the design of an intelligent robot for real-time monitoring of plant nutrients within greenhouse environments. The robot’s ability to detect and address iron deficiency in spinach plants was evaluated. To achieve this, the robot, equipped with a camera, captured visible imaging from surface of spinach plants including both the control group and the group subjected to iron deficiency stress. Statistical analysis of the data was performed at the probability level of 5 %, and genetic algorithm (GA) was utilized to select the most suitable features (G, Area, Energy, Entropy, R and L), from 41 extracted image parameters, including color, morphological, and texture features. An artificial neural network (ANN) was then employed to detect iron deficiency in plants, resulting in an accuracy of 96 %. Subsequently, the Gaussian kernel function of support vector regression (SVR) algorithm was used to predict spinach iron content, yielding the highest determination coefficient (R2 = 0.94) and the lowest values of mean absolute percentage error (MAPE = 5.92), root mean square error (RMSE = 0.43) and standard regression error (SRE = 2.66). The designed robot detected iron-deficient plants with precision, sensitivity, specificity and accuracy of 86 %, 82 %, 84 % and 83 %, respectively. The robot then took the necessary control actions to supply the deficiency of this nutrient by foliar spraying of more than 89 % of the crop area.

Investigation of LSTM Model in Stock Prices Prediction During the COVID-19 Based on Smartphone Brands

The unforeseen outbreak of the COVID-19 pandemic in early 2020 had a profound impact on the real economy and business sectors, leading to a period of heightened volatility. The stock price of smartphone brands had shown an abnormal trend of fluctuation and hard to be predicted by using the inchoate regression and machine learning models. In this paper, Long Short-Term Memory (LSTM) is adapted to predict the stock price of five top smartphone brands. Spanning the period from 2016 to 2021, the dataset for each brand contains 1258 data points, which are split into two groups, training set including 850 observations and test set including 408 observations after the pandemic in 2020. The model employed two prices as x and the next price as y to be predicted. The structure of the model in this work is composed of 3 layers, with 64 and 5 neurons in the first two LSTM layers respectively and a dense layer for dense equal to 1. The model is based on TensorFlow system with Adaptive Moment Estimation optimizer and Mean Absolute Error as the loss function. For the model checking, Root Mean Standard Error, Mean Absolute Error and R-square score are calculated to evaluate the precision of the prediction. Experimental results indicate that under an unexpected external condition, LSTM is effective in stock price prediction to a certain extent. Further investigations are still needed to improve LSTM applied in the stock market.

Multi-Axis Laser Interferometer Not Affected by Installation Errors Based on Nonlinear Computation

Most multi-axis laser interferometers require accurate installation of the lasers and detectors since the position and orientation installation errors of lasers and detectors bring measurement error to the interferometer. In this paper, the multi-axis interferometer based on nonlinear computation is proposed, which avoids the measurement error caused by installation errors by taking the laser position and orientation as unknowns into the measurement model and discussing the solution component uniqueness of a nonlinear equation system. The simulation results show that even when the installation errors are close to 1 μm and 1 μrad, the proposed interferometer can still measure the multi-degree-of-freedom displacement accurately, and the root mean standard error (RMSE) of displacement is 1.884 nm and 5.871 × 10−7 mrad under a reading noise level of 0.1 nm.

Development and Test of Rice Nitrogen Nutrition Index Estimation Model Based on Airborne Multi-Spectrum

Both excessive and deficient nitrogen (N) concentration in the growing media can affect the growth, development, yield, and quality of rice. Traditional methods of N determination of plant require destructive and rigorous sampling, which is also time-consuming and laborious. However, rapid and non-destructive nitrogen diagnosis has become an important area of research in precision agriculture. Heilongjiang Province, China is a cold climate rice growing area, where the growth and fertilization of rice follows a definite pattern. In the present study, two varieties of rice (Wuyoudao4 and Songjing9) and as location Heilongjiang Province were selected. Nitrogen diagnosis of rice was carried out based on airborne multi-spectrum methodology. Canopy spectral data of rice at key growth periods were obtained by using a UAV equipped with a multi-spectral camera, and agronomic parameters such as leaf N content and dry matter weight were obtained synchronically. An airborne multispectral canopy normalized vegetation index (NDVI) model for N diagnosis based on its critical concentration curve was established as a nondestructive N diagnosis of rice for cold region. Results showed that canopy NDVI can estimate rice nitrogen nutrition index (NNI) properly over the growth period. The coefficient of determination R2, root mean square error (RMSE) and standard root mean square error (nRMSE) were compared to determine the best effect of the index model. The interphase nitrogen diagnosis model of WYD-4 based on NDVI for cold region was as follows: NNI=0.3916e1.0809*NDVI (RMSE=0.12, nRMSE=12.43%), SJ-9: NNI=0.3325e1.2705*NDVI (RMSE=0.10, nRMSE =10.36%), indicating that the established model can better estimate the nitrogen status of rice. Bangladesh J. Bot. 52(2): 529-538, 2023 (June) Special

Near-infrared spectroscopy-based quantification of sunflower oil and pea protein isolate in dense mixtures for novel plant-based products

Techniques to quantify oil and protein in plant-based products are laborious and environmentally harmful. This study explores the potential of near-infrared (NIR) spectroscopy as an alternative method for rapid and non-destructive quantification of oil and protein in mixtures with known content of sunflower oil and pea protein isolate (PPI). Accurate calibrations with partial least square regression (PLSR) were possible for sunflower oil (Root Mean Standard Error of the Test (RMSET) set, 0.33%; coefficient of determination of the test set (R2t), 0.99) and PPI content (RMSET, 1.24%; R2t, 0.99). Prediction of an extrapolated validation set was less accurate for sunflower oil (Root Mean Standard Error of the Validation (RMSEV) set, 2.65%; coefficient of determination of the validation set (R2v), 0.70), but was successful for PPI content (RMSEV, 2.43%; R2v, 0.93). Wavelength range reduction for CH/NH bonds improved the predictions of the PLSR models for oil but not for PPI. Wavelength selection using the Covariance Selection (CovSel) algorithm reduced the number of wavelengths from 2151 to 3 without loss of prediction accuracy within the calibration range. Wavelength range reduction gave the best model for oil; wavelength selection was best for PPI. NIR spectroscopy is a promising tool to determine oil and protein in plant-based foods rapidly and with sufficient accuracy.

Modelling Reaeration Coefficient of Stream using Regression Analytical Method - A case of Mmubete Stream, Rivers State Nigeria

Surface water pollution is a major problem/ occurrence in the Niger Delta region of Nigeria. Mmubete stream is never an exception. Mmubete stream is significant to the people of Rivers State owing to its usefulness in terms fishing activities and domestic usage. The modelling of Mmubete stream using regression analytical method with emphasis on incorporating mixing properties of stream in stream reaeration prediction was carried out. Water samples were collected and analysed for dissolved oxygen (DO) and temperature. The hydrodynamic data (depth, velocity, surface area, kinematic viscosity and dispersion) of the stream were measured while re-aeration coefficient using empirical models developed using regression analytical approach was determined. The results revealed that the field reaeration coefficient of Mmubete stream ranges from 2.4432d-1 to 3.7568d-1 in the wet season and 0.96d-1 to 2.712d-1 in the dry season. The reaeration coefficient of the stream ranges from 1.983d-1 to 3.088d-1 using the model 1 for the prediction, 1.983d-1 to 3.5065d-1 using the model 2 and 3.0221d-1 to d-1 4.1817 using the model 3. The R2 of the models are 0.934, 0.934 and 0.998 for models 1, 2 and 3 respectively and the standard errors are 0.11135, 0.549694 and 0.022008 for model 1,2 and 3 respectively. The models developed are reliable considering the root mean square and standard error values.

A nonlinear autoregressive exogenous neural network (NARX-NN) model for the prediction of solvent-based oil extraction from Hura crepitans seeds

Abstract Vegetable oils are a crucial source of raw materials for many industries. In order to meet the rising demand for oil on global scale, it has become essential to investigate underutilized plant oilseeds. Hura crepitans seeds are one of the underused plant oilseeds from which oil can be produced via solvent-based extraction. For the purpose of predicting the oil yield from Hura crepitans seeds, the extraction process was modelled using a nonlinear autoregressive exogenous neural network (NARX-NN). The input variables to the model are seed/solvent ratio, extraction temperature and extraction time, while oil yield is the response output variable. NARX-NN model is based on 200 data samples, and model architecture comprises of 3 inputs, 1 hidden layer (with 15 neurons) and 1 output with 2 delay elements. The performance evaluation was carried out to examine the accuracy of the developed model in predicting oil yield from Hura crepitans using different statistical indices. The overall correlation coefficient, R (0.80829), mean square error, MSE (0.0120), root mean square error, RMSE (0.1080) and standard prediction error, SEP (0.1666) show that NARX-NN model can accurately be used for the prediction oil yield from Hura crepitans seeds.

Semantic stereo visual SLAM toward outdoor dynamic environments based on ORB-SLAM2

PurposeThe prerequisite for most traditional visual simultaneous localization and mapping (V-SLAM) algorithms is that most objects in the environment should be static or in low-speed locomotion. These algorithms rely on geometric information of the environment and restrict the application scenarios with dynamic objects. Semantic segmentation can be used to extract deep features from images to identify dynamic objects in the real world. Therefore, V-SLAM fused with semantic information can reduce the influence from dynamic objects and achieve higher accuracy. This paper aims to present a new semantic stereo V-SLAM method toward outdoor dynamic environments for more accurate pose estimation.Design/methodology/approachFirst, the Deeplabv3+ semantic segmentation model is adopted to recognize semantic information about dynamic objects in the outdoor scenes. Second, an approach that combines prior knowledge to determine the dynamic hierarchy of moveable objects is proposed, which depends on the pixel movement between frames. Finally, a semantic stereo V-SLAM based on ORB-SLAM2 to calculate accurate trajectory in dynamic environments is presented, which selects corresponding feature points on static regions and eliminates useless feature points on dynamic regions.FindingsThe proposed method is successfully verified on the public data set KITTI and ZED2 self-collected data set in the real world. The proposed V-SLAM system can extract the semantic information and track feature points steadily in dynamic environments. Absolute pose error and relative pose error are used to evaluate the feasibility of the proposed method. Experimental results show significant improvements in root mean square error and standard deviation error on both the KITTI data set and an unmanned aerial vehicle. That indicates this method can be effectively applied to outdoor environments.Originality/valueThe main contribution of this study is that a new semantic stereo V-SLAM method is proposed with greater robustness and stability, which reduces the impact of moving objects in dynamic scenes.

Infrared spectroscopy for the quality assessment of Habanero chilli: a proof-of-concept study

Habanero chillies (Capsicum chinense cv Habanero) are a popular species of hot chilli in Australia, with production steadily increasing. However, there is limited research on this crop due to its relatively low levels of production at present. Rapid methods of assessing fruit quality could be greatly beneficial both for quality assurance purposes and for use in breeding programs or experimental growing trials. This work investigated the potential of using infrared spectroscopy for the quality assessment of 20 Australian Habanero chilli samples, including the prediction of dry matter content, total phenolic content, ferric reducing antioxidant potential and capsaicin/dihydrocapsaicin content. Near-infrared spectra (908–1676 nm) taken from the fresh fruit showed strong potential for the estimation of dry matter content, with a root mean standard error of cross-validation (RMSECV) of 0.50% FW. A moving-window partial least squares regression model was applied to optimise the spectral window used for dry matter content prediction, with the best-performing window being between 1224 and 1422 nm. However, the near-infrared spectra of fresh fruit could not be used to estimate the total phenolic content or capsaicin/dihydrocapsaicin content of the samples. Mid-infrared spectra (4000 − 400 cm− 1) collected from the dried, powdered material showed slightly more promise for the prediction of total phenolics and the ratio of capsaicin:dihydrocapsaicin, with a R2cv of 0.45 and RMSECV of 0.32 for the latter parameter. Although further refinement is required, this method may be able to detect samples with high/low contents of total phenolics or for the estimation of the capsaicin:dihydrocapsaicin ratio.

Determining the optimum climate preseason for plant phenology analysis using rubber (Hevea brasiliensis) as a model

ABSTRACT Identifying the optimum preseason that best explains subsequent plant phenology is essential to understanding how climatic factors influence plant growth. This study evaluated the preseason (30, 60, 90, 120, and 180-day) influence of average temperature and rainfall accumulation on the two primary phenological events for rubber (Hevea brasiliensis): refoliation, or start of season (SOS), and defoliation, or end of the season (EOS). These phenological events were derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Different Vegetation Index (NDVI) time-series data over a ten-year period (2010 to 2019). Pearson correlation and multiple linear regression analyses were performed using these datasets to determine the optimal climate preseason for rubber phenology, based on the highest coefficient results. The results showed that the 90-day preseason conditions prior to the SOS and EOS were critical in advancing or delaying the start and end of the rubber season compared to other preseasons, indicated by the best combination of moderate to high correlation values. The 90-day preseason had the highest coefficient of determination (R 2 ) and lowest root mean standard error (RMSE), confirming its utility for identifying preseason conditions for studying rubber phenology.

Possibilities of River Water Temperature Reconstruction Using Statistical Models in the Context of Long-Term Thermal Regime Changes Assessment

Water temperature in rivers is the key property determining the biotic and abiotic processes occurring in these ecosystems. In many regions of the world, the significant lack of measurement data is a serious problem. This paper presents reconstruction of water temperature for selected Polish rivers with monitoring discontinued in the period 2015–2020. Information regarding air temperature and water temperature in lakes provided the basis for the comparison of three models: multiple linear regression, random forest regression, and multilayer perceptron network. The results show that the best reconstruction results were obtained with a multilayer perceptron network model based on water temperatures in the lake and air temperatures from three meteorological stations. The average values of mean error, root mean square error and standard error were for the rivers in Poland: 1.52 °C, 5.03%, and 0.47 °C. The course of mean yearly water temperature in the years 1987–2020 showed a statistically significant increase from 0.18 to 0.49 °C per decade. The results show that the largest increases occurred in June, August, September, November, and December.

Paddy moisture on-line detection based on ensemble preprocessing and modeling for combine harvester

The on-line detection of paddy moisture content (MC) during harvest has gained increasing interest recently due to its unique role for the control of combine harvester, yield evaluation and post-harvest grain handling operations. However, it is very difficult to achieve good performance under the complex and changeable situation during field harvest. In this study, paddy varieties, paddy flow, feeding types and algorithms were comprehensively considered to optimize the MC detection method. Firstly, an on-line near-infrared sensing system supplemented for grain tank of combine harvester was designed, and spectra were collected under the most common and essential detecting conditions, which including paddy varieties, feeding types and straw effect. Then, ensemble preprocessing, parameter optimization and accuracy test were performed. The best result of all conditions was extreme learning machine (ELM) coupled with the ensemble preprocessing of orthogonal signal correction with savitzky-golay (OSC + SG). The root mean standard error of prediction (RMSEPV) of this method after validation on unknown sample was as low as 1.0791% w.b, and the residual predictive deviation (RPDV) was higher than 3.5646. Stability tests were carried out under conditions of varying feeding types and straw quantities. The results showed that ELM had enough robustness to cope with complex detecting conditions and maintain proper accuracy as the mean value of repeatability, conditions and reproducibility were calculated as 0.0213%, 0.4471% and 0.6868% w.b, respectively. Despite the preliminary feasibility for on-line MC measurement of paddy, the on-line near-infrared sensing system needs to be verified on combine harvester during harvest.