Classification and Prediction by Pigment Content in Lettuce (Lactuca sativa L.) Varieties Using Machine Learning and ATR-FTIR Spectroscopy

Influence of sample preparation methods on FTIR spectra for taxonomic identification of tropical trees in the Atlantic forest

The Atlantic forest is one of the world's major tropical biomes due to its rich biodiversity. Its vast diversity of plant species poses challenges in floristic surveys. Fourier transform infrared spectroscopy (FTIR) enables rapid and residue-free data collection, providing diverse applications in organic sample analysis. FTIR spectra quality depends on the sample preparation methodology. However, no research on FTIR spectroscopy methodology for taxonomy has been conducted with tropical tree species. Hence, this study addresses the sample preparation influence on FTIR spectra for the taxonomic classification of 12 tree species collected in the Serra do Mar State Park (PESM) - Cunha Nucleus - São Paulo State, Brazil. Spectra were obtained from intact fresh (FL), intact dried (DL), and heat-dried ground (GL) leaves. The spectra were evaluated through chemometrics using Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and Linear Discriminant Analysis (LDA) with validation by LDA-PCA. The results demonstrate that sample preparation directly influences tropical species FTIR spectra categorization capability. The best taxonomic classification result for all techniques, validated by LDA-PCA, was obtained from GL. FTIR spectra evaluation through PCA, HCA, and LDA allow for the observation of phylogenetic relationships among the species. FTIR spectroscopy proves to be a viable technique for taxonomic evaluation of tree species in floristic exploration of tropical biomes which can complement traditional tools used for taxonomic studies.

A pilot study on non-invasive in situ detection of phytochemicals and plant endogenous status using fiber optic infrared spectroscopy

Traditional methods for assessing plant health often lack the necessary attributes for continuous and non-destructive monitoring. In this pilot study, we present a novel technique utilizing a customized fiber optic probe based on attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) with a contact force control unit for non-invasive and continuous plant health monitoring. We also developed a normalized difference mid-infrared reflectance index through statistical analysis of spectral features, enabling differentiation of drought and age conditions in plants. Our research aims to characterize phytochemicals and plant endogenous status optically, addressing the need for improved analytical measurement methods for in situ plant health assessment. The probe configuration was optimized with a triple-loop tip and a 3 N contact force, allowing sensitive measurements while minimizing leaf damage. By combining polycrystalline and chalcogenide fiber probes, a comprehensive wavenumber range analysis (4000-900 cm-1) was achieved. Results revealed significant variations in phytochemical composition among plant species, for example, red spinach with the highest polyphenolic content and green kale with the highest lignin content. Petioles displayed higher lignin and cellulose absorbance values compared to veins. The technique effectively monitored drought stress on potted green bok choy plants in situ, facilitating the quantification of changes in water content, antioxidant activity, lignin, and cellulose levels. This research represents the first demonstration of the potential of fiber optic ATR-FTIR probes for non-invasive and rapid plant health measurements, providing insights into plant health and advancements in quantitative monitoring for indoor farming practices, bioanalytical chemistry, and environmental sciences.

The Visibility of Changes in the Antioxidant Compound Profiles of Strawberry and Raspberry Fruits Subjected to Different Storage Conditions Using ATR-FTIR and Chemometrics

Strawberry cultivars Portola and Enduro, as well as raspberry cultivars Enrosadira and Kwazi, were evaluated for their antioxidant potential after treatment with gaseous ozone and different refrigeration storage conditions. Their antioxidant capacity was investigated with ABTS and DPPH methods, and the chemical composition was determined by measuring the total phenolic (TPC) and flavonoid (TFC) compounds. The classification of different samples of berry puree was influenced significantly by both the cultivars and the refrigeration storage method. Moreover, FTIR spectroscopy coupled with chemometrics was used as an alternative technique to conventional methods to determine the chemical composition of strawberries and raspberries. The chemometric discrimination of samples was achieved using principal component analysis (PCA), hierarchical clustering analysis (HCA) and linear discriminant analysis (LDA) modelling procedures performed on the FTIR preprocessed spectral data for the fingerprint region (1800-500 cm-1). The fingerprint range between 1500 and 500 cm-1, corresponding to deformation vibrations from polysaccharides, pectin and organic acid content, had a significant impact on the grouping of samples. The results obtained by PCA-LDA scores revealed a clear separation between four classes of samples and demonstrated a high overall classification rate of 97.5% in differentiating between the raspberry and strawberry cultivars.

Non-Invasive Assessment, Classification, and Prediction of Biophysical Parameters Using Reflectance Hyperspectroscopy

Hyperspectral technology offers significant potential for non-invasive monitoring and prediction of morphological parameters in plants. In this study, UV-VIS-NIR-SWIR reflectance hyperspectral data were collected from Nicotiana tabacum L. plants using a spectroradiometer. These plants were grown under different light and gibberellic acid (GA₃) concentrations. Through spectroscopy and multivariate analyses, key growth parameters, such as height, leaf area, energy yield, and biomass, were effectively evaluated based on the interaction of light with leaf structures. The shortwave infrared (SWIR) bands, specifically SWIR1 and SWIR2, showed the strongest correlations with these growth parameters. When classifying tobacco plants grown under different GA₃ concentrations in greenhouses, artificial intelligence (AI) and machine learning (ML) algorithms were employed, achieving an average accuracy of over 99.1% using neural network (NN) and gradient boosting (GB) algorithms. Among the 34 tested vegetation indices, the photochemical reflectance index (PRI) demonstrated the strongest correlations with all evaluated plant phenotypes. Partial least squares regression (PLSR) models effectively predicted morphological attributes, with R²_CV values ranging from 0.81 to 0.87 and RPD_P values exceeding 2.09 for all parameters. Based on Pearson's coefficient XYZ interpolations and HVI algorithms, the NIR-SWIR band combination proved the most effective for predicting height and leaf area, while VIS-NIR was optimal for optimal energy yield, and VIS-VIS was best for predicting biomass. To further corroborate these findings, the SWIR bands for certain morphological characteristic wavelengths selected with s-PLS were most significant for SWIR1 and SWIR2, while i-PLS showed a more uniform distribution in VIS-NIR-SWIR bands. Therefore, SWIR hyperspectral bands provide valuable insights into developing alternative bands for remote sensing measurements to estimate plant morphological parameters. These findings underscore the potential of remote sensing technology for rapid, accurate, and non-invasive monitoring within stationary high-throughput phenotyping systems in greenhouses. These insights align with advancements in digital and precision technology, indicating a promising future for research and innovation in this field.

Reflectance Spectroscopy for the Classification and Prediction of Pigments in Agronomic Crops

Reflectance spectroscopy, in combination with machine learning and artificial intelligence algorithms, is an effective method for classifying and predicting pigments and phenotyping in agronomic crops. This study aims to use hyperspectral data to develop a robust and precise method for the simultaneous evaluation of pigments, such as chlorophylls, carotenoids, anthocyanins, and flavonoids, in six agronomic crops: corn, sugarcane, coffee, canola, wheat, and tobacco. Our results demonstrate high classification accuracy and precision, with principal component analyses (PCAs)-linked clustering and a kappa coefficient analysis yielding results ranging from 92 to 100% in the ultraviolet-visible (UV-VIS) to near-infrared (NIR) to shortwave infrared (SWIR) bands. Predictive models based on partial least squares regression (PLSR) achieved R² values ranging from 0.77 to 0.89 and ratio of performance to deviation (RPD) values over 2.1 for each pigment in C₃ and C₄ plants. The integration of pigment phenotyping methods with fifteen vegetation indices further improved accuracy, achieving values ranging from 60 to 100% across different full or range wavelength bands. The most responsive wavelengths were selected based on a cluster heatmap, β-loadings, weighted coefficients, and hyperspectral vegetation index (HVI) algorithms, thereby reinforcing the effectiveness of the generated models. Consequently, hyperspectral reflectance can serve as a rapid, precise, and accurate tool for evaluating agronomic crops, offering a promising alternative for monitoring and classification in integrated farming systems and traditional field production. It provides a non-destructive technique for the simultaneous evaluation of pigments in the most important agronomic plants.

Biophysical, Biochemical, and Photochemical Analyses Using Reflectance Hyperspectroscopy and Chlorophyll a Fluorescence Kinetics in Variegated Leaves

The adjustments that occur during photosynthesis are correlated with morphological, biochemical, and photochemical changes during leaf development. Therefore, monitoring leaves, especially when pigment accumulation occurs, is crucial for monitoring organelles, cells, tissue, and whole-plant levels. However, accurately measuring these changes can be challenging. Thus, this study tests three hypotheses, whereby reflectance hyperspectroscopy and chlorophyll a fluorescence kinetics analyses can improve our understanding of the photosynthetic process in Codiaeum variegatum (L.) A. Juss, a plant with variegated leaves and different pigments. The analyses include morphological and pigment profiling, hyperspectral data, chlorophyll a fluorescence curves, and multivariate analyses using 23 JIP test parameters and 34 different vegetation indexes. The results show that photochemical reflectance index (PRI) is a useful vegetation index (VI) for monitoring biochemical and photochemical changes in leaves, as it strongly correlates with chlorophyll and nonphotochemical dissipation (Kn) parameters in chloroplasts. In addition, some vegetation indexes, such as the pigment-specific simple ratio (PSSRc), anthocyanin reflectance index (ARI1), ratio analysis of reflectance spectra (RARS), and structurally insensitive pigment index (SIPI), are highly correlated with morphological parameters and pigment levels, while PRI, moisture stress index (MSI), normalized difference photosynthetic (PVR), fluorescence ratio (FR), and normalized difference vegetation index (NDVI) are associated with photochemical components of photosynthesis. Combined with the JIP test analysis, our results showed that decreased damage to energy transfer in the electron transport chain is correlated with the accumulation of carotenoids, anthocyanins, flavonoids, and phenolic compounds in the leaves. Phenomenological energy flux modelling shows the highest changes in the photosynthetic apparatus based on PRI and SIPI when analyzed with Pearson's correlation, the hyperspectral vegetation index (HVI) algorithm, and the partial least squares (PLS) to select the most responsive wavelengths. These findings are significant for monitoring nonuniform leaves, particularly when leaves display high variation in pigment profiling in variegated and colorful leaves. This is the first study on the rapid and precise detection of morphological, biochemical, and photochemical changes combined with vegetation indexes for different optical spectroscopy techniques.

A Novel Method for Estimating Chlorophyll and Carotenoid Concentrations in Leaves: A Two Hyperspectral Sensor Approach

Leaf optical properties can be used to identify environmental conditions, the effect of light intensities, plant hormone levels, pigment concentrations, and cellular structures. However, the reflectance factors can affect the accuracy of predictions for chlorophyll and carotenoid concentrations. In this study, we tested the hypothesis that technology using two hyperspectral sensors for both reflectance and absorbance data would result in more accurate predictions of absorbance spectra. Our findings indicated that the green/yellow regions (500-600 nm) had a greater impact on photosynthetic pigment predictions, while the blue (440-485 nm) and red (626-700 nm) regions had a minor impact. Strong correlations were found between absorbance (R2 = 0.87 and 0.91) and reflectance (R2 = 0.80 and 0.78) for chlorophyll and carotenoids, respectively. Carotenoids showed particularly high and significant correlation coefficients using the partial least squares regression (PLSR) method (R2C = 0.91, R2cv = 0.85, and R2P = 0.90) when associated with hyperspectral absorbance data. Our hypothesis was supported, and these results demonstrate the effectiveness of using two hyperspectral sensors for optical leaf profile analysis and predicting the concentration of photosynthetic pigments using multivariate statistical methods. This method for two sensors is more efficient and shows better results compared to traditional single sensor techniques for measuring chloroplast changes and pigment phenotyping in plants.

Interpretable machine learning assisted spectroscopy for fast characterization of biomass and waste

The combination of machine learning and infrared spectroscopy was reported as effective for fast characterization of biomass and waste (BW). However, this characterization process is lack of interpretability towards its chemical insights, leading to less satisfactory recognition for its reliability. Accordingly, this paper aimed to explore the chemical insights of the machine learning models in the fast characterization process. A novel dimensional reduction method with significant physicochemical meanings was thus proposed, where the high loading spectral peaks of BW were selected as input features. Combined with functional groups attribution of these spectral peaks, the machine learning models established based on the dimensionally reduced spectral data could be explained with clear chemical insights. The performance of classification and regression models between the proposed dimensional reduction method and principal component analysis method was compared. The influence mechanism of each functional group on the characterization results were discussed. CH deformation, CC stretch & CO stretch and ketone/aldehyde CO stretch played essential roles in C, H/ LHV and O prediction, respectively. The results of this work demonstrated the theoretical fundamentals of the machine learning and spectroscopy based BW fast characterization method.

Enhancing Pigment Phenotyping and Classification in Lettuce through the Integration of Reflectance Spectroscopy and AI Algorithms

In this study, we investigated the use of artificial intelligence algorithms (AIAs) in combination with VIS-NIR-SWIR hyperspectroscopy for the classification of eleven lettuce plant varieties. For this purpose, a spectroradiometer was utilized to collect hyperspectral data in the VIS-NIR-SWIR range, and 17 AIAs were applied to classify lettuce plants. The results showed that the highest accuracy and precision were achieved using the full hyperspectral curves or the specific spectral ranges of 400-700 nm, 700-1300 nm, and 1300-2400 nm. Four models, AdB, CN2, G-Boo, and NN, demonstrated exceptional R² and ROC values, exceeding 0.99, when compared between all models and confirming the hypothesis and highlighting the potential of AIAs and hyperspectral fingerprints for efficient, precise classification and pigment phenotyping in agriculture. The findings of this study have important implications for the development of efficient methods for phenotyping and classification in agriculture and the potential of AIAs in combination with hyperspectral technology. To advance our understanding of the capabilities of hyperspectroscopy and AIs in precision agriculture and contribute to the development of more effective and sustainable agriculture practices, further research is needed to explore the full potential of these technologies in different crop species and environments.