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15 pages, 4344 KiB

Open AccessArticle

Comprehensive Assessment of the Dynamics of Banana Chilling Injury by Advanced Optical Techniques

by Werner B. Herppich and Tamás Zsom

Appl. Sci. 2021, 11(23), 11433; https://0-doi-org.brum.beds.ac.uk/10.3390/app112311433 - 02 Dec 2021

Cited by 2 | Viewed by 4353

Green-ripe banana fruit are sensitive to chilling injury (CI) and, thus, prone to postharvest quality losses. Early detection of CI facilitates quality maintenance and extends shelf life. CI affects all metabolic levels, with membranes and, consequently, photosynthesis being primary targets. Optical techniques such [...] Read more.

Green-ripe banana fruit are sensitive to chilling injury (CI) and, thus, prone to postharvest quality losses. Early detection of CI facilitates quality maintenance and extends shelf life. CI affects all metabolic levels, with membranes and, consequently, photosynthesis being primary targets. Optical techniques such as chlorophyll a fluorescence analysis (CFA) and spectroscopy are promising tools to evaluate CI effects in photosynthetically active produce. Results obtained on bananas are, however, largely equivocal. This results from the lack of a rigorous evaluation of chilling impacts on the various aspects of photosynthesis. Continuous and modulated CFA and imaging (CFI), and VIS remission spectroscopy (VRS) were concomitantly applied to noninvasively and comprehensively monitor photosynthetically relevant effects of low temperatures (5 °C, 10 °C, 11.5 °C and 13 °C). Detailed analyses of chilling-related variations in photosynthetic activity and photoprotection, and in contents of relevant pigments in green-ripe bananas, helped to better understand the physiological changes occurring during CI, highlighting that distinct CFA and VRS parameters comprehensively reflect various effects of chilling on fruit photosynthesis. They revealed why not all CFA parameters can be applied meaningfully for early detection of chilling effects. This study provides relevant requisites for improving CI monitoring and prediction. Full article

(This article belongs to the Special Issue Applications of Optical Spectroscopy in Plant Sciences)

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12 pages, 1692 KiB

Open AccessFeature PaperArticle

Winter Wheat Take-All Disease Index Estimation Model Based on Hyperspectral Data

by Wei Guo, Yifeng Yang, Hengqian Zhao, Rui Song, Ping Dong, Qian Jin, Muhammad Hasan Ali Baig, Zelong Liu and Zihan Yang

Appl. Sci. 2021, 11(19), 9230; https://0-doi-org.brum.beds.ac.uk/10.3390/app11199230 - 04 Oct 2021

Cited by 4 | Viewed by 2004

Abstract

Wheat take-all, caused by two variants of the fungus Gaeumannomyces gramnis (Sacc.) Arx & D. Olivier, was common in spring wheat areas in northwest and north China and occurred in winter wheat areas in north China. The yield of common disease areas [...] Read more.

Wheat take-all, caused by two variants of the fungus Gaeumannomyces gramnis (Sacc.) Arx & D. Olivier, was common in spring wheat areas in northwest and north China and occurred in winter wheat areas in north China. The yield of common disease areas was reduced by more than 20% and the yield of severe cases was reduced by more than 50%. Large-scale rapid and accurate estimation of the incidence of wheat take-all plays an important role in guiding field control and agricultural yield estimation. In this study, a portable ground spectrometer was used to collect the spectral reflectance in the 350–1050 nm band range of wheat canopy after take-all infection in the wheat grain filling stage and combined with the ground disease survey data.Then a winter wheat take-all disease index estimation model was proposed based on the spectral band division interval and selected band combination. According to the normalized difference spectral index (NDSI) and the determinative coefficient of the disease index formed by any two band combinations, the spectral index band combinations corresponding to the spectral index with high correlation in each region were screened by dividing spectral intervals. Partial least-squares regression was used to establish a binary and ternary disease index calibration model. The results showed that the model based on spectral indices of ternary variables had the highest coefficient of determination. Finally, the optimal regression model of wheat take-all disease condition index composed of NDSI(R₅₉₀,R₅₉₈), NDSI(R₅₃₄,R₇₄₂) and NDSI(R₈₁₀,R₈₃₄) was established: Y = 134.577 − 70.301 NDSI(R₅₉₀,R₅₉₈) − 223.533 NDSI(R₅₃₄,R₇₄₂) + 51.584 NDSI(R₈₁₀,R₈₃₄) (R² = 0.743, RMSEP = 0.094, df = 15), which was the most suitable model for winter wheat take-all estimation. The construction of this model can provide new method and technical support for future evaluation and monitoring of wheat take-all disease on the field. Full article

(This article belongs to the Special Issue Applications of Optical Spectroscopy in Plant Sciences)

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15 pages, 60556 KiB

Open AccessArticle

Comparative FT-IR Prospecting for Cellulose in Stems of Some Fiber Plants: Flax, Velvet Leaf, Hemp and Jute

by Rodica Vârban, Ioana Crișan, Dan Vârban, Andreea Ona, Loredana Olar, Andrei Stoie and Răzvan Ștefan

Appl. Sci. 2021, 11(18), 8570; https://0-doi-org.brum.beds.ac.uk/10.3390/app11188570 - 15 Sep 2021

Cited by 22 | Viewed by 3638

Abstract

Plant fibers are sustainable sources of materials for many industries, and can be obtained from a variety of plants. Cellulose is the main constituent of plant-based fibers, and its properties give the characteristics of the fibers obtained. Detailed characterization of cellulosic fibers is [...] Read more.

Plant fibers are sustainable sources of materials for many industries, and can be obtained from a variety of plants. Cellulose is the main constituent of plant-based fibers, and its properties give the characteristics of the fibers obtained. Detailed characterization of cellulosic fibers is often performed after lengthy extraction procedures, while fast screening might bring the benefit of quick qualitative assessment of unprocessed stems. The aim of this research was to define some marker spectral regions that could serve for fast, preliminary qualitative characterization of unprocessed stems from some textile plants through a practical and minimally invasive method without lengthy extraction procedures. This could serve as a screening method for sorting raw materials by providing an accurate overall fingerprint of chemical composition. For this purpose, we conducted comparative Fourier Transform Infrared Spectroscopy (FT-IR) prospecting for quality markers in stems of flax (Linum usitatissimum L.), velvet leaf (Abutilon theophrasti Medik.), hemp (Cannabis sativa L.) and jute (Corchorus olitorius L.). Analysis confirmed the presence of major components in the stems of the studied plants. Fingerprint regions for cellulose signals were attributed to bands at 1420–1428 cm⁻¹ assigned to the crystalline region and 896–898 cm⁻¹ assigned to the amorphous region of cellulose. The optimization of characterization methods for raw materials is important and can find immediate practical applications. Full article

(This article belongs to the Special Issue Applications of Optical Spectroscopy in Plant Sciences)

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15 pages, 2894 KiB

Open AccessFeature PaperArticle

Comparing Machine Learning Methods for Classifying Plant Drought Stress from Leaf Reflectance Spectra in Arabidopsis thaliana

by Ana Barradas, Pedro M.P. Correia, Sara Silva, Pedro Mariano, Margarida Calejo Pires, Ana Rita Matos, Anabela Bernardes da Silva and Jorge Marques da Silva

Appl. Sci. 2021, 11(14), 6392; https://0-doi-org.brum.beds.ac.uk/10.3390/app11146392 - 11 Jul 2021

Cited by 14 | Viewed by 2979

Abstract

Plant breeders and plant physiologists are deeply committed to high throughput plant phenotyping for drought tolerance. A combination of artificial intelligence with reflectance spectroscopy was tested, as a non-invasive method, for the automatic classification of plant drought stress. Arabidopsis thaliana plants (ecotype Col-0) [...] Read more.

Plant breeders and plant physiologists are deeply committed to high throughput plant phenotyping for drought tolerance. A combination of artificial intelligence with reflectance spectroscopy was tested, as a non-invasive method, for the automatic classification of plant drought stress. Arabidopsis thaliana plants (ecotype Col-0) were subjected to different levels of slowly imposed dehydration (S0, control; S1, moderate stress; S2, severe stress). The reflectance spectra of fully expanded leaves were recorded with an Ocean Optics USB4000 spectrometer and the soil water content (SWC, %) of each pot was determined. The entire data set of the reflectance spectra (intensity vs. wavelength) was given to different machine learning (ML) algorithms, namely decision trees, random forests and extreme gradient boosting. The performance of different methods in classifying the plants in one of the three drought stress classes (S0, S1 and S2) was measured and compared. All algorithms produced very high evaluation scores (F1 > 90%) and agree on the features with the highest discriminative power (reflectance at ~670 nm). Random forests was the best performing method and the most robust to random sampling of training data, with an average F1-score of 0.96 ± 0.05. This classification method is a promising tool to detect plant physiological responses to drought using high-throughput pipelines. Full article

(This article belongs to the Special Issue Applications of Optical Spectroscopy in Plant Sciences)

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16 pages, 1298 KiB

Open AccessArticle

NIRS Estimation of Drought Stress on Chemical Quality Constituents of Taro (Colocasia esculenta L.) and Sweet Potato (Ipomoea batatas L.) Flours

by Carla S. S. Gouveia, Vincent Lebot and Miguel Pinheiro de Carvalho

Appl. Sci. 2020, 10(23), 8724; https://0-doi-org.brum.beds.ac.uk/10.3390/app10238724 - 05 Dec 2020

Cited by 6 | Viewed by 2308

Abstract

Taro (Colocasia esculenta (L.) Schott) and sweet potato (Ipomoea batatas (L.) Lam.) are important food crops worldwide, whose productivity is threatened by climatic constraints, namely drought. Data calibration, validation, and model development of high-precision near-infrared spectroscopy (NIRS) involving multivariate analyses are [...] Read more.

Taro (Colocasia esculenta (L.) Schott) and sweet potato (Ipomoea batatas (L.) Lam.) are important food crops worldwide, whose productivity is threatened by climatic constraints, namely drought. Data calibration, validation, and model development of high-precision near-infrared spectroscopy (NIRS) involving multivariate analyses are needed for the fast prediction of the quality of tubers and shoots impacted by drought stress. The main objective of this study was to generate accurate NIRS models for quality assessment of taro and sweet potato accessions (acc.) subjected to water scarcity conditions. Seven taro and eight sweet potato acc. from diverse geographical origins were evaluated for nitrogen (N), protein (Pt), starch (St), total mineral (M), calcium oxalate (CaOx), carbon isotope discrimination (Δ¹³C), and nitrogen isotopic composition (δ¹⁵N). Models were developed separately for both crops underground and aboveground organs. N, Pt, St, and M models could be used as quality control constituents, with a determination coefficient of prediction (r²_pred) between 0.856 and 0.995. δ¹³C, δ¹⁵N, and CaOx, with r²_pred between 0.178 and 0.788, could be used as an informative germplasm screening tool. The approach used in the present study demonstrates NIRS’s potential for further research on crop quality under drought. Full article

(This article belongs to the Special Issue Applications of Optical Spectroscopy in Plant Sciences)

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16 pages, 2990 KiB

Open AccessArticle

Determination of the Most Effective Wavelengths for Prediction of Fuji Apple Starch and Total Soluble Solids Properties

by Razieh Pourdarbani, Sajad Sabzi, Sanaz Jarolmasjed and Thomas Panagopoulos

Appl. Sci. 2020, 10(22), 8145; https://0-doi-org.brum.beds.ac.uk/10.3390/app10228145 - 17 Nov 2020

Cited by 4 | Viewed by 2099

Abstract

Proper physical properties and standard chemical properties are among the criteria that consumers use to select fruits. Recently, researchers attempted to develop non-destructive methods for measuring properties, among which the near-infrared (NIR) spectroscopy is of great use. Fuji apples were collected in three [...] Read more.

Proper physical properties and standard chemical properties are among the criteria that consumers use to select fruits. Recently, researchers attempted to develop non-destructive methods for measuring properties, among which the near-infrared (NIR) spectroscopy is of great use. Fuji apples were collected in three different growth stages, and then starch and soluble solids were extracted. Spectral data in the range of 800 to 900 nm were used to predict the amount of starch content and 920 to 980 nm to estimate total soluble solids (TSS). Reflectance spectra were pre-processed and the most effective wavelengths of each property were selected using hybrid artificial neural network-simulated annealing (ANN-SA). Non-destructive estimation of physicochemical properties was conducted using spectral data of the most effective wavelengths using a hybrid artificial neural network-biogeography-based optimization algorithm (ANN-BBO). The results indicated that the regression coefficient of the best state of training for predicting starch was 0.97 and of TSS was 0.96, while R² was 0.92 for both. The most effective wavelengths were 852.58, 855.54, 849.03, 855.83, 853.47, 844.90 nm for starch and 967.86, 966.67, 964.90, 958.40, 957.22, 963.97 nm for TSS. Full article

(This article belongs to the Special Issue Applications of Optical Spectroscopy in Plant Sciences)

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21 pages, 4418 KiB

Open AccessArticle

Glyphosate-Based Herbicide Toxicophenomics in Marine Diatoms: Impacts on Primary Production and Physiological Fitness

by Ricardo Cruz de Carvalho, Eduardo Feijão, Ana Rita Matos, Maria Teresa Cabrita, Sara C. Novais, Marco F. L. Lemos, Isabel Caçador, João Carlos Marques, Patrick Reis-Santos, Vanessa F. Fonseca and Bernardo Duarte

Appl. Sci. 2020, 10(21), 7391; https://0-doi-org.brum.beds.ac.uk/10.3390/app10217391 - 22 Oct 2020

Cited by 17 | Viewed by 3039

Abstract

Glyphosate is the main active component of the commercial formulation Roundup^®, the most widely used chemical herbicide worldwide. However, its potential high toxicity to the environment and throughout trophic webs has come under increasing scrutiny. The present study aims to investigate [...] Read more.

Glyphosate is the main active component of the commercial formulation Roundup^®, the most widely used chemical herbicide worldwide. However, its potential high toxicity to the environment and throughout trophic webs has come under increasing scrutiny. The present study aims to investigate the application of bio-optical techniques and their correlation to physiological and biochemical processes, including primary productivity, oxidative stress, energy balance, and alterations in pigment and lipid composition in Phaeodactylum tricornutum, a representative species of marine diatoms, using the case study of its response to the herbicide glyphosate-based Roundup^® formulation, at environmentally relevant concentrations. Cultures were exposed to the herbicide formulation representing effective glyphosate concentrations of 0, 10, 50, 100, 250, and 500 μg L⁻¹. Results showed that high concentrations decreased cell density; furthermore, the inhibition of photosynthetic activity was not only caused by the impairment of electron transport in the thylakoids, but also by a decrease of antioxidant capacity and increased lipid peroxidation. Nevertheless, concentrations of one of the plastidial marker fatty acids had a positive correlation with the highest concentration as well as an increase in total protein. Cell energy allocation also increased with concentration, relative to control and the lowest concentration, although culture growth was inhibited. Pigment composition and fatty acid profiles proved to be efficient biomarkers for the highest glyphosate-based herbicide concentrations, while bio-optical data separated controls from intermediate concentrations and high concentrations. Full article

(This article belongs to the Special Issue Applications of Optical Spectroscopy in Plant Sciences)

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22 pages, 930 KiB

Open AccessEditor’s ChoicePerspective

Making Sense of Light: The Use of Optical Spectroscopy Techniques in Plant Sciences and Agriculture

by Ana M. Cavaco, Andrei B. Utkin, Jorge Marques da Silva and Rui Guerra

Appl. Sci. 2022, 12(3), 997; https://0-doi-org.brum.beds.ac.uk/10.3390/app12030997 - 19 Jan 2022

Cited by 15 | Viewed by 3755

Abstract

As a result of the development of non-invasive optical spectroscopy, the number of prospective technologies of plant monitoring is growing. Being implemented in devices with different functions and hardware, these technologies are increasingly using the most advanced data processing algorithms, including machine learning [...] Read more.

As a result of the development of non-invasive optical spectroscopy, the number of prospective technologies of plant monitoring is growing. Being implemented in devices with different functions and hardware, these technologies are increasingly using the most advanced data processing algorithms, including machine learning and more available computing power each time. Optical spectroscopy is widely used to evaluate plant tissues, diagnose crops, and study the response of plants to biotic and abiotic stress. Spectral methods can also assist in remote and non-invasive assessment of the physiology of photosynthetic biofilms and the impact of plant species on biodiversity and ecosystem stability. The emergence of high-throughput technologies for plant phenotyping and the accompanying need for methods for rapid and non-contact assessment of plant productivity has generated renewed interest in the application of optical spectroscopy in fundamental plant sciences and agriculture. In this perspective paper, starting with a brief overview of the scientific and technological backgrounds of optical spectroscopy and current mainstream techniques and applications, we foresee the future development of this family of optical spectroscopic methodologies. Full article

(This article belongs to the Special Issue Applications of Optical Spectroscopy in Plant Sciences)

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