Application of reflectance spectroscopies (FTIR-ATR & FT-NIR) coupled with multivariate methods for robust in vivo detection of begomovirus infection in papaya leaves

Identification of spectral ranges that contribute to phytoplasma detection in apple trees - A step towards an on-site method

'Candidatus Phytoplasma mali' is the bacterial agent associated with Apple Proliferation, a disease that causes high economic losses in affected commercial apple growing regions. The identification of the disease is carried out by visual inspection performed by skilled professionals in the orchards. To confirm an infection, costly molecular laboratory methods must be applied. Furthermore, both methods are very time-consuming. Here, we analysed the potential of a non-destructive method using in-field measurements to differentiate infected from non-infected apple trees (Malus domestica) based on spectral signatures of fresh leaves. By using multivariate statistics, we were able to distinguish infected from non-infected trees and identified the wavelengths relevant for the differentiation. Factors affecting the differentiation performance were the sampling date and bacterial colonization behaviour.

Insights and protocols for discrimination of sugarcane clones by dissimilarity measures on RGB and NIR data

In sugarcane breeding, dense experiments have been considered in the initial phase (T1), such as the Simplified System (SS) of genotype selection. In this method, the seedlings of each family are transplanted directly from the seed box to the field, forming a kind of carpet. Despite the practical aspect of the method, selection problems are common, as stalks from the same individual within the family are subject to being taken to later evaluation stages, to the detriment of stalks from different individuals. To facilitate the discrimination of stalks of the same family in SS, we evaluated using RGB images (red:green:blue) and NIR (near infrared) spectra. We applied Euclidean distance (D) and Mahalanobis distance (D2) dissimilarity measures to the image and spectral data to distinguish stalks with different genotypes. RGB and NIR data were taken from type +1 leaf samples collected from two experimental blocks, totaling 31 evaluated families. The analyzes were carried out in two stages. In the first stage, we sought to evaluate the classification capacity using RGB images and NIR spectra, using D as a measure of dissimilarity. In the second step, we developed and validated a protocol using RGB images to classify clones, with D2 as a dissimilarity measure. Preliminary results, with distance D, allowed to discriminate clones based on the distance of the evaluated attributes and their combinations. In addition, with the analyzes using the D distance, it was identified that only the use of the R attribute (red band) would give satisfactory results for the second stage, which was the proposed analysis protocol, applying the D2 distance. The D2 statistic and associated p-value confirmed the protocol's usefulness in discriminating stalks in SS, especially stalks from the same families.

ATR-FTIR spectroscopy and chemometrics as a quick and simple alternative for discrimination of SARS-CoV-2 infected food of animal origin

Alternative routes such as virus transmission or cross-contamination by food have been suggested, due to reported cases of SARS-CoV-2 in frozen chicken wings and fish or seafood. Delay in routine testing due to the dependence on the PCR technique as the standard method leads to greater virus dissemination. Therefore, alternative detection methods such as FTIR spectroscopy emerge as an option. Here, we demonstrate a fast (3 min), simple and reagent-free methodology using attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy for discrimination of food (chicken, beef and fish) contaminated with the SARS-CoV-2 virus. From the IR spectra of the samples, the "bio-fingerprint" (800 - 1900 cm-1) was selected to investigate the distinctions caused by the virus contamination. Exploratory analysis of the spectra, using Principal Component of Analysis (PCA), indicated the differentiation in the data due to the presence of single bands, marked as contamination from nucleic acids including viral RNA. Furthermore, the partial least squares discriminant analysis (PLS-DA) classification model allowed for discrimination of each matrix in its pure form and its contaminated counterpart with sensitivity, specificity and accuracy of 100 %. Therefore, this study indicates that the use of ATR-FTIR can offer a fast and low cost and not require chemical reagents and with minimal sample preparation to detect the SARS-CoV-2 virus in food matrices, ensuring food safety and non-dissemination by consumers.

Rapid On-site identification of geographical origin and storage age of tangerine peel by Near-infrared spectroscopy

The feasibility of identifying geographical origin and storage age of tangerine peel was explored by using a handheld near-infrared (NIR) spectrometer combined with machine learning. A handheld NIR spectrometer (900-1700 nm) was used to scan the outer surface of tangerine peel and collect the corresponding NIR diffuse reflectance spectra. Principal component analysis (PCA) combined with Mahalanobis distance were used to detect outliers. The accuracies of all models in the anomaly set were much lower than that in calibration set and test set, indicating that the outliers were effectively identified. After removing the outliers, in order to initially explore the clustering characteristics of tangerine peels, PCA was performed on tangerine peels from different origins and the same origin with different storage ages. The results showed that the tangerine peels from the same origin or the same storage age had the potential to cluster, indicating that the spectral data of the same origin or the same storage age had a certain similarity, which laid the foundation for subsequent modeling and identification. However, there were quite a few samples with different origins or different storage ages overlapped and could not be distinguished from each other. In order to achieve qualitative identification of origin and storage age, Savitzky-Golay convolution smoothing with first derivative (SGFD) and standard normal variate (SNV) were used to preprocess the raw spectra. Random forest (RF), K-nearest neighbor (KNN) and linear discriminant analysis (LDA) were used to establish the discriminant model. The results showed that SGFD-LDA could accurately distinguish the origin and storage age of tangerine peel at the same time. The origin identification accuracy was 96.99%. The storage age identification accuracy was 100% for Guangdong tangerine peel and 97.15% for Sichuan tangerine peel. This indicated that the near-infrared spectroscopy (NIRS) combine with machine learning can simultaneously and rapidly identify the origin and storage age of tangerine peel on site.

Potential Use of Papaya Waste as a Fuel for Bioelectricity Generation

Papaya (Carica papaya) waste cause significant commercial and environmental damage, mainly due to the economic losses and foul odours they emit when decomposing. Therefore, this work provides an innovative way to generate electricity for the benefit of society and companies dedicated to the import and export of this fruit. Microbial fuel cells are a technology that allows electricity generation. These cells were produced with low-cost materials using zinc and copper electrodes; while a 150 mL polymethylmethacrylate tube was used as a substrate collection chamber (papaya waste). Maximum values of 0.736 ± 0.204 V and 5.57 ± 0.45 mA were generated, while pH values increased from 3.848 to 8.227 ± 0.35 and Brix decreased slowly from the first day. The maximum power density value was 878.38 mW/cm2 at a current density of 7.245 A/cm2 at a maximum voltage of 1072.77 mV. The bacteria were identified with an identity percentage of 99.32% for Achromobacter xylosoxidans species, 99.93% for Acinetobacter bereziniae, and 100.00% for Stenotrophomonas maltophilia. This research gives a new way for the use of papaya waste for bioelectricity generation.

Ganoderma boninense Disease Detection by Near-Infrared Spectroscopy Classification: A Review

Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causes a serious threat to the palm oil industry. This catastrophic disease ultimately destroys the basal tissues of oil palm, causing the eventual death of the palm. Early detection of G. boninense is vital since there is no effective treatment to stop the continuing spread of the disease. This review describes past and future prospects of integrated research of near-infrared spectroscopy (NIRS), machine learning classification for predictive analytics and signal processing towards an early G. boninense detection system. This effort could reduce the cost of plantation management and avoid production losses. Remarkably, (i) spectroscopy techniques are more reliable than other detection techniques such as serological, molecular, biomarker-based sensor and imaging techniques in reactions with organic tissues, (ii) the NIR spectrum is more precise and sensitive to particular diseases, including G. boninense, compared to visible light and (iii) hand-held NIRS for in situ measurement is used to explore the efficacy of an early detection system in real time using ML classifier algorithms and a predictive analytics model. The non-destructive, environmentally friendly (no chemicals involved), mobile and sensitive leads the NIRS with ML and predictive analytics as a significant platform towards early detection of G. boninense in the future.

Aquaphotomics-From Innovative Knowledge to Integrative Platform in Science and Technology

Aquaphotomics is a young scientific discipline based on innovative knowledge of water molecular network, which as an intrinsic part of every aqueous system is being shaped by all of its components and the properties of the environment. With a high capacity for hydrogen bonding, water molecules are extremely sensitive to any changes the system undergoes. In highly aqueous systems-especially biological-water is the most abundant molecule. Minute changes in system elements or surroundings affect multitude of water molecules, causing rearrangements of water molecular network. Using light of various frequencies as a probe, the specifics of water structure can be extracted from the water spectrum, indirectly providing information about all the internal and external elements influencing the system. The water spectral pattern hence becomes an integrative descriptor of the system state. Aquaphotomics and the new knowledge of water originated from the field of near infrared spectroscopy. This technique resulted in significant findings about water structure-function relationships in various systems contributing to a better understanding of basic life phenomena. From this foundation, aquaphotomics started integration with other disciplines into systematized science from which a variety of applications ensued. This review will present the basics of this emerging science and its technological potential.