Fast and low-cost method for direct and simultaneous determination of nitrogen and carbon in soybean leaves using benchtop and portable near-infrared devices.

BACKGROUND: The current techniques for determining carbon and nitrogen content to provide information about the nutritional status of plants are time-consuming and expensive. For this reason, the objective of this study was to develop an analytical method for the direct and simultaneous determination of nitrogen and carbon elemental content in soybean leaves using near-infrared spectroscopy and compare the performance of conventional (1100-2500 nm spectral range) and portable equipment (1100-1700 nm spectral range). Partial least-squares regression models were developed using 27 soybean leaf samples collected during the 2021 harvest and applied for the simultaneous determination of carbon and nitrogen in 13 samples collected during the 2022 harvest. RESULTS: The root-mean-square error of prediction values for nitrogen and carbon were low (2.42 g kg-1 and 4.37 g kg-1 respectively) for the benchtop method yielded low but higher for the portable method (3.82 g kg-1 and 10.7 g kg-1 respectively). The benchtop method did not show significant differences when compared with the reference method for determining nitrogen and carbon. In contrast, the portable methodology showed potential as a screening method for determining nitrogen levels, particularly in fieldwork. CONCLUSION: The methodologies evaluated in this study were implemented and evaluated under real crop monitoring conditions, using independent sets of calibration and prediction samples. Their utilization enables the acquisition of cost-effective, safe analytical data aligning with the principles of green analytical chemistry. © 2023 Society of Chemical Industry.

The application of parallel processing in the selection of spectral variables in beer quality control.

Parallel data analysis was investigated to improve performance in variable selection and to develop predictive models for beer quality control. A set of spectral near infrared (NIR) data from 60 beer samples and its primitive extracts as the original concentration was used. The dataset was distributed to Raspberry Pi 3 Model B devices connected to a network that was running a Machine Learning service. With more than 4 devices acting in parallel, it was possible to reduce time in 57% to find the best linear regression coefficient (0.999) with the lower RMSECV (0.216) if compared to a singular desktop computer. Thus, parallel processing can significantly reduce the time to indicate the best model fitted during the variable's selection.

IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context Histories.

The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing, more accurate sensors, and specific protocols for the Internet of Things (IoT). One of the trends in this area of research is the use of context awareness. In agriculture, the context involves the environment, for example, the conditions found inside a greenhouse. Recently, a series of studies have proposed the use of sensors to monitor production and/or the use of cameras to obtain information about cultivation, providing data, reminders, and alerts to farmers. This article proposes a computational model for indoor agriculture called IndoorPlant. The model uses the analysis of context histories to provide intelligent generic services, such as predicting productivity, indicating problems that cultivation may suffer, and giving suggestions for improvements in greenhouse parameters. IndoorPlant was tested in three scenarios of the daily life of farmers with hydroponic production data that were obtained during seven months of cultivation of radicchio, lettuce, and arugula. Finally, the article presents the results obtained through intelligent services that use context histories. The scenarios used services to recommend improvements in cultivation, profiles and, finally, prediction of the cultivation time of radicchio, lettuce, and arugula using the partial least squares (PLS) regression technique. The prediction results were relevant since the following values were obtained: 0.96 (R2, coefficient of determination), 1.06 (RMSEC, square root of the mean square error of calibration), and 1.94 (RMSECV, square root of the mean square error of cross validation) for radicchio; 0.95 (R2), 1.37 (RMSEC), and 3.31 (RMSECV) for lettuce; 0.93 (R2), 1.10 (RMSEC), and 1.89 (RMSECV) for arugula. Eight farmers with different functions on the farm filled out a survey based on the technology acceptance model (TAM). The results showed 92% acceptance regarding utility and 98% acceptance for ease of use.

Rapid, Noninvasive, and Nondestructive Method for Biofilm Imaging on Metallic Surfaces Using Active Thermography.

A simple, rapid, low-cost method was proposed for the imaging of Pseudomonas aeruginosa biofilms on metallic surfaces using an infrared camera. Stainless steel coupons were cooled to generate a thermal gradient in relation to biofilm for active thermography (AT). Both cooling and image acquisition times were optimized and the images obtained with AT were compared with those from scanning electron microscopy. A free software (Thermofilm) was developed for image processing and the results were compared with the software ImageJ, with good agreement (from 87.7 to 103.8%). Images of coupons treated with sanitizer (peracetic acid) were obtained to show the applicability of the proposed method for biofilm studies. All analytical steps could be performed in 3 min in a noncontact, nondestructive, low-cost, portable, and easy-to-use way.

Infrared enthalpymetric methods: A new, fast and simple alternative for sodium determination in food sauces.

This work developed a new technique and an application of an existing approach to determine sodium in food sauces, involving enthalpimetric reactions in the infrared. Infrared Thermometric Titration (TT-IR) was utilized, with simple analyzers and low-cost measurement instruments for the acquisition of the surface temperature generated in the sodium precipitation reaction and development of software for the acquisition and processing of data using Raspberry Pi. The sodium was also quantified by Thermal Infrared Enthalpimetry (TIE), a recently developed technique. The rapid and simple quantification of sodium by the TT-IR and TIE showed the possibility of a selective reaction for sodium, using aluminum nitrate, potassium and ammonium fluoride in an acid medium, with reduction of the reagents and without the digestion step in the sample preparation. The results acquired through TT-IR and TIE corroborated the Flame Atomic Emission Spectrometry (FAES) with 96 to 103% and 95 to 102%, respectively.

Flow thermal infrared enthalpimetry: Rapid and inexpensive determination of the alcohol content of distilled beverages.

In this work, a simple and inexpensive flow thermal infrared enthalpimetry (TIE-F) method was developed through a combination of flow injection analysis and thermal infrared enthalpimetry (TIE) to determine the alcohol content of distilled beverages (cachaça, cognac, and vodka). The principle used in this method consisted of monitoring the enthalpy of dissolution of ethanol through a low-cost infrared sensor coupled to a flow system. The results showed an agreement between the proposed method and the conventional method, ranging from 96.5% to 99.0%. The obtained limit of quantification (LOQ, 10σ) of 25.10% (v/v) was enough for distilled beverages, which should present a minimum alcoholic content of 36% according to Brazilian legislation. In addition, the TIE-F instrumentation presented costs that were 100 times lower than the instrumentation that was used in the batch TIE, and the time of analysis was significantly reduced. The amount of residue generated was also reduced, thereby providing significant energy savings and easy adaptation to processes on industrial scales.