A Review of the Latest Spectroscopic Research in Agriculture Analysis

The Latest Spectroscopic Research in Agriculture Analysis © Dzikir - stock.adobe.com

Introduction

Spectroscopic analytical techniques are vital in the agricultural sciences, offering powerful tools for the detailed classification and quantification of various natural and farm produced products. Techniques such as inductively coupled plasma mass spectrometry (ICP-MS) and inductively coupled plasma optical emission spectroscopy (ICP-OES) are utilized for trace elemental analysis, providing high sensitivity and precision. Raman spectroscopy, including its enhanced variant, surface-enhanced Raman spectroscopy (SERS), is employed for molecular imaging, fingerprinting, and detecting low concentrations of important parameters. Fourier transform infrared (FT-IR) and near-infrared (NIR) spectroscopic techniques are used for identifying chemical bonds and functional groups within molecules. X-ray fluorescence (XRF) is applied to assess the presence of various elements in agricultural materials. Ultraviolet-visible spectroscopy (UV-vis) measures absorbance and concentration of analytes, while fluorescence spectroscopy detects the emission of light by substances, often used for tracking molecular interactions, and trace components. Nuclear magnetic resonance (NMR) spectroscopy offers detailed information about molecular structure and conformational subtleties through the interaction of nuclear spin properties following the application of an external magnetic field.

In recent years, the advancement of analytical techniques has significantly improved our ability to assess the safety, quality, and origin of agricultural products, alongside monitoring environmental impacts such as soil contamination. This review article synthesizes findings from multiple studies that have employed various spectroscopic methods to address these critical issues.

ICP-MS has been effectively utilized in combination with principal component analysis (PCA) to authenticate the geographical origin of agricultural products, advocating for the development of international standards to streamline the process and enhance trade efficiency. The method's precision is also highlighted in environmental monitoring, as demonstrated in a 2024 study comparing ICP-MS with atomic absorption spectrophotometry (AAS) and portable XRF (pXRF) for assessing copper contamination in soils, confirming pXRF's reliability as a cost-effective alternative. XRF technology's potential in soil nutrient assessment is also discussed, highlighting its application in developing predictive models for calcium and potassium content in soils.

The review further explores the application of ICP-OES in determining heavy metal contamination in agricultural soils and wild fruits, emphasizing the method's role in identifying moderate to low contamination levels and correlating these findings with environmental and anthropogenic sources. Raman spectroscopy, particularly SERS, emerges as a powerful tool for detecting heavy metals in food products, providing rapid and non-invasive analysis crucial for consumer safety.

FT-IR spectroscopy, coupled with machine learning (ML) algorithms, has shown promise in rapidly analyzing lignocellulosic content in agricultural waste, offering potential for bioenergy production and sustainable material development. NIR spectroscopy is also discussed, with its applications in in-field soil analysis and its ability to rapidly assess soil fertility parameters, making it a valuable tool for precision agriculture.

UV-vis spectroscopy has been explored for its role in dye adsorption studies using carbon derived from agricultural waste, as well as in the authentication of green coffee beans with Protected Designation of Origin (PDO) certification. The versatility and accessibility of UV-vis spectroscopy make it a widely adopted technique in agricultural research.

Fluorescence spectroscopy and imaging are highlighted for their non-destructive capabilities in assessing the quality of agricultural products. This review discusses the use of fluorescence techniques in detecting fruit ripeness, sugar content, and peel defects, providing valuable insights into the rapid and accurate evaluation of food quality.

NMR spectroscopy, a non-destructive technique known for its detailed molecular-level insights, has been utilized to characterize complex plant substances and monitor soil health. The review highlights the application of NMR in analyzing the chemical composition of soils and plants, particularly in detecting and quantifying organic matter and pollutants.

This review article underscores the versatility and importance of these analytical methods in modern agriculture, food safety, and environmental monitoring, advocating for continued research and the establishment of standardized protocols to enhance the accuracy and applicability of these techniques across different agricultural contexts. The following text outlines the different spectroscopic analytical techniques and applications.

ICP-MS

A 2023 review examines the application of ICP-MS combined with PCA for determining the geographical origin of agricultural food products. The article highlights that ICP-MS with PCA is an effective and widely adopted method for authenticating and certifying the geographic origin of plant-based food products (1). The paper advocates for the development of an international standard to streamline this process, reduce analysis time and costs, and enhance trade efficiency. It outlines the necessary steps for establishing such standards, including developing guidelines and quality control measures, to ensure accurate product information and safety in food markets (1).

Copper (Cu) accumulation in agricultural soils is persistent and non-biodegradable, necessitating regular monitoring through selective, rapid, and cost-effective analytical methods. Over the past decade, portable X-ray fluorescence spectrometers (pXRF) have seen significant performance improvements and are increasingly used in environmental and soil sciences to replace inductively coupled plasma mass spectrometry (ICP-MS) and atomic absorption spectrophotometry (AAS) measurements for routine and field analysis of mineral content. A 2024 study compares Cu content in agricultural soil samples using three analytical techniques: ICP-MS, AAS, and pXRF. Thirty-five soil samples from vineyards, arable land, and a meadow in North-West Croatia were analyzed. The Cu content detected by pXRF under laboratory conditions showed strong agreement with results from ICP-MS and AAS, with a high positive correlation (R² ≈ 0.996 for ICP-MS and 0.997 for AAS) and similar concentration ranges across methods. The findings suggest that pXRF is a reliable tool for analyzing Cu in contaminated agricultural soils (2).

Nanoparticle (NP) applications aiming to boost plant biomass production and enhance the nutritional quality of crops have proven to be a valuable ally in enhancing agricultural output. They contribute to greater food accessibility for a growing and vulnerable human population. These nanoscale particles are commonly used in agriculture as fertilizers, pesticides, plant growth promoters, seed treatments, opportune plant disease detection, monitoring soil and water quality, identification and detection of toxic agrochemicals, and soil and water remediation. In addition to the countless NP applications in food and agriculture, it is possible to highlight many others, such as medicine and electronics.

However, it is crucial to emphasize the imperative need for thorough NP characterization beyond these applications. Therefore, analytical methods are proposed to determine NPs’ physicochemical properties, such as composition, crystal structure, size, shape, surface charge, morphology, and specific surface area, by using inductively coupled plasma mass spectrometry (ICP-MS) that allows the reliable elemental composition quantification mainly in metallic NPs. As a result, a review published in 2022 highlights studies involving NPs in agriculture and their consequential effects on plants, with a specific focus on analyses conducted using ICP-MS. Given the numerous applications of NPs in the field, it is essential to address their increased presence in the environment and in human exposure since biomagnification and biotransformation effects are phenomena that should be further studied. Considering the increased use of NPs, the demand for rapid, innovative, and sensitive analytical methods for the characterization of these NPs in the environment remains paramount (3).

ICP-OES

A study in 2023 aimed to assess the contamination levels of twelve heavy metals (HMs) in agricultural soils from Kafr El-Zayat, Egypt, using ICP-OES to measure concentrations of magnesium (Mg), vanadium (V), chromium (Cr), manganese (Mn), iron (Fe), cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn), arsenic (As), strontium (Sr), and lead (Pb). Various geochemical indices, including contamination factor (CF), enrichment factor (EF), geo-accumulation index (Igeo), degree of contamination (Cd), and pollution load index (PLI), were used to evaluate contamination levels (4). The results indicated that the soils were generally moderately contaminated with V, Cr, Co, Cu, Zn, and Sr (1 ≤ CF < 3), while contamination levels were low for Mg, Mn, Fe, Ni, and Pb (CF < 1). Geo-accumulation indices suggested that the soils were largely uncontaminated, except for V, which showed mild pollution (0 < Igeo < 1). The soils had deficient to minimal enrichment of HMs (EF < 2). Cluster and PCAs grouped the HMs into three clusters, suggesting multiple pollution sources. Anthropogenic contributions to V, Cr, Co, Cu, Zn, As, and Sr were estimated to be between 11% and 45% of total metal content. Overall, the soils were classified as moderately contaminated (Cd = 13.7) and slightly polluted (PLI = 1.1) (4).

A 2023 research paper quantified macro and micro elements and heavy metals accumulated in wild fruits analyzed by ICP-OES. The study noted that the moisture content in wild fruits ranged from 51.07% in rosehip to 88.49% in yellow raspberry. Wild strawberry exhibited the highest levels of phosphorus (P), potassium (K), and calcium (Ca), while Mg was most abundant in wild strawberry, fig, and rosehip. Phosphorus and potassium concentrations in these fruits varied widely, with P ranging from 385 mg/kg in blackberry to 2538 mg/kg in fig, and K from 6114 mg/kg in medlar to 18,613 mg/kg in wild strawberry. Among the microelements, Zn, Cu, Mn, and boron (B) were found in the highest quantities, while Fe levels were relatively low, ranging from 0.21 mg/kg in apple to 1.32 mg/kg in fig. Zn and Cu concentrations were recorded between 1.39 mg/kg in apple and 16.4 mg/kg in fig, and between 1.54 mg/kg in apple and 18.4 mg/kg in wild strawberry, respectively, with Cu levels in raspberry (both red and yellow), blackberry, jujube, and medlar being similar (5). As levels were notably higher than other elements, likely due to soil contamination with As-rich industrial waste. As concentrations in wild fruits ranged from 4.21 µg/g in rosehip to 65.7 µg/g in wild strawberry, while aluminum (Al) content varied from 0.03 µg/g in rosehip to 16.0 µg/g in white mulberry (5). Overall ICP-OES was quite useful for these analyses.

Raman

Heavy metals in the human body can cause physiological toxicity by disrupting protein and enzyme transport, making their detection crucial for food and agricultural product safety. A 2023 review paper highlights recent advancements in detecting heavy metals in food and agricultural products using surface-enhanced Raman spectroscopy (SERS). The paper discusses the basic principles of SERS and its application in detecting metals like mercury (Hg), As, cadmium (Cd), Pb, and Cr. The review also explores the integration of chemometrics and multivariate analysis with SERS, along with the development of novel SERS substrates at both macro and micro scales. The review study emphasizes SERS as a powerful technique due to its simplicity, rapid data collection, and non-invasive nature, and it explores future applications of SERS in heavy metal detection for food and agricultural materials (6).

Raman spectroscopy is increasingly recognized as a leading non-destructive method for qualitative and quantitative analysis of plant substances, offering rapid molecular structure measurement without the need for pretreatment. The technique's sensitivity depends on factors such as laser wavelength, power intensity, and exposure time. In plant samples, fluorescence can obscure target material peaks, prompting the use of low-energy lasers (785 nm or near-infrared) to reduce fluorescence. Advanced methods like surface-enhanced Raman spectroscopy, time-gated Raman spectroscopy, and combinations with thin-layer chromatography are being explored for improved spectra quality. Spectral analysis methods, crucial for accurate plant quality assessment, are being optimized for broader application. This review covers the latest advances in micro-Raman spectroscopy measurements for assessing crop quality, particularly secondary metabolites, from in vitro to in vivo and in situ, and suggests directions for future research to achieve more universal applications (7).

As consumers become more health-conscious, the demand for high-quality and safe fruits and vegetables has increased, highlighting the need for rapid detection methods. A study from 2023 reviews five Raman spectroscopy techniques, detailing their principles, advantages, and progress in detecting fruit and vegetable quality and safety. Raman spectroscopy has been shown to effectively and quickly identify classification, ripeness, freshness, disease infestation, and surface pesticide residue contamination. It can also analyze the content and distribution of material components within fruits and vegetables. A 2023 paper discusses the challenges and technological limitations currently faced in applying Raman spectroscopy for analysis of fruits and vegetables and explores future development trends to expand the application of Raman for fruit and vegetable quality and safety testing (8).

X-ray (XRF)

Timely diagnosis of plant-available soil nutrients is essential for improving agricultural productivity and reducing yield gaps. There is a global need for practical, user-friendly analytical tools that can quickly, cost-effectively, and environmentally sustainably assess soil nutrient status. Recent research highlights the potential of energy dispersive X-ray fluorescence (XRF) sensors for monitoring agricultural soils. A 2023 study reviews the feasibility of using XRF sensors for predicting plant-available nutrients and the study identifies key challenges such as mitigating the matrix effect in XRF spectral libraries and developing calibration models that account for the local context of the total-to-available nutrient ratio (T/A ratio) (9). The study suggests future research directions, including understanding how soil management affects the temporal stability of the T/A ratio and XRF model performance, exploring advanced predictive modeling strategies to address XRF challenges, and optimizing data acquisition and modeling strategies for in situ use of portable XRF sensors. Addressing these challenges is crucial for advancing the technology's maturity and improving its application in soil nutrient prediction. Portable, easy-to-use analytical tools are vital for enhancing soil health monitoring and informing best management practices, particularly in regions with limited soil laboratory infrastructure. Effective soil monitoring is key to preserving and sustaining soil health, which is critical for soil and food security (9).

Soil sample matrix variations across different regions can hinder the accuracy of nutrient analysis using XRF sensors, and few effective strategies have been proposed to address this issue. A 2023 study evaluated the performance of various predictive models—simple linear regression (RS), without Compton normalization (designated RS1) and with Compton normalization (designated RS2); multiple linear regression (MLR), partial least-squares regression (PLS), and random forest (RF). The calibration models were used for predicting Ca and K levels in agricultural soils. The RS models were tested for both RS1 and RS2. Additionally, the study explored whether incorporating soil texture information and vis–NIR spectra as auxiliary variables could enhance model predictions (10).

The findings showed that all methods somewhat mitigated the matrix effect, achieving strong predictive performance for Ca and K content (R² ≥ 0.84). The RS2 model excelled in predicting Ca (R² = 0.92, Root mean square error (RMSE) = 48.25 mg/kg, with a 52.3% improvement over RS1), while the RF model was most effective for predicting K (R² = 0.84, RMSE = 17.43 mg/kg, with a 24.3% improvement over RS1). Notably, advanced models did not always outperform simpler linear models. Incorporating auxiliary data was beneficial for K prediction, reducing errors by 10%, but had minimal impact on Ca prediction, with less than a 1% error reduction. The study concludes that the optimal modeling approach depends on the specific attribute being predicted, offering valuable insights for developing intelligent modeling strategies in sensor-based soil analysis (10).

FT-IR

Efficiently converting lignocellulosic waste into value-added products is crucial for sustainable development. However, separating the cellulose-hemicellulose-lignin network in lignocellulosic waste is challenging due to its robust structure. A study from 2023 uses FT-IR spectroscopy for rapid, non-invasive analysis of cellulose and lignin in farm waste from approximately 30 different crops. FT-IR spectra peak heights for cellulose and lignin related absorbance bands correlate linearly with reference concentrations over a narrow range, revealing 44 ± 5% cellulose and 9.95 ± 2% lignin in a specific variety of rice straw, though there is high variability across species (11). Due to the compositional complexity, FT-IR data is analyzed using machine learning (ML) models, with the random forest (RF) algorithm achieving the best classification accuracy (0.75) for species-wide cellulose and lignin content. Convolutional neural network (CNN) modeling with Bayesian regularization further effectively represents the lignocellulose data, with root mean square error (RMSE) ~0.11). The study also examines the impact of different pre-treatments on cellulose structure using FT-IR peak analysis, finding that pre-treatment with deep eutectic solvent significantly improves cellulose accessibility, enhancing glucose yield by 38% compared to acid and alkali pre-treatments (11).

Macroalgae, including Eucheuma denticulatum, Solieria chordalis (red algae), and Sargassum muticum (brown alga), offer significant potential as sources of nutrition and biologically active compounds due to their abundant biomass. A 2023 study focuses on extracting and characterizing cell wall polysaccharides—carrageenans, fucoidans, and alginates—from these macroalgae using attenuated total reflectance/reflection sampling with Fourier transform infrared spectroscopy (ATR FT-IR). The comparison of purified extracts with commercial polysaccharides revealed strong spectral similarities, confirming the effectiveness of the extraction methods and validating ATR FT-IR as a rapid, non-destructive analysis technique (12).

The study also demonstrated that FT-IR could detect seasonal variations in the composition of these polysaccharides. PCA was able to highlight structural changes related to the harvest period, indicating that the bioactivity of algal polysaccharides varies with the algae's growth cycle. Specifically, S. chordalis and E. denticulatum were found to predominantly produce iota-carrageenan at maturity, while precursor forms were detectable during earlier growth stages. Additionally, traces of kappa-carrageenan and nu-carrageenan were identified in juvenile E. denticulatum, providing insights into optimizing the extraction process for various applications (12).

NIR

In-field soil spectroscopy offers a rapid, non-destructive method for analyzing soil properties directly in the field using visible to near-infrared (vis–NIR) spectroscopy for the spectral range of 350–2500 nm. This vis-NIR technique enables quick data collection from numerous samples without chemical processing, which is especially beneficial for agriculture. A 2024 review paper evaluates the current state of in-field vis–NIR spectroscopy, identifying gaps in measurement reliability and robustness (13). The article covers various aspects including sensor ranges, carrier platforms, sensor types, measurement distances, and methodologies. The review also discusses the use of different instruments and their spectral capabilities, and the potential for cross-calibration with laboratory soil spectral libraries. Commonly analyzed soil properties include carbon content, texture, nitrogen, pH, and cation exchange capacity. Future developments should focus on expanding databases, integrating diverse instruments and cropping systems, and creating standardized measurement protocols to enhance the practical application of this technology (13).

Visible and near-infrared (vis–NIR) spectroscopy has become increasingly popular for assessing soil fertility due to its speed and cost-effectiveness. A 2024 study evaluates best practices for using vis–NIR spectroscopy to measure key soil fertility parameters (texture, organic carbon, pH, cation exchange capacity, and major nutrients) in the field. Comparisons were made between spectra from different scanning positions using two types of portable spectrometers: a microelectromechanical systems (MEMS)-based and a research-grade spectrometer. Spectra were collected from the cutaway side of soil samples, raw soil surfaces, and cleaned soil surfaces at 134 sampling points (14). Partial least squares regression (PLSR) models were developed for each parameter, scanning position, and spectral pretreatment. Results showed successful prediction of clay, sand, pH, organic carbon, cation exchange capacity, total nitrogen, and exchangeable magnesium. However, total and exchangeable Ca, K, P, and total Mg were not predicted satisfactorily. The best scanning position was the cutaway side of soil cores, with the research-grade spectrometer generally outperforming the MEMS-based instrument. Nonetheless, the MEMS-based spectrometer still provided acceptable predictions. The recommended practice for in-situ soil vis–NIR scans involves scanning along the cutaway side of soil cores with at least five replicates (14).

UV-vis

Research published in 2023 provides a comprehensive guide for conducting dye adsorption experiments using carbon derived from agricultural waste. It details a step-by-step procedure, starting with the preparation of biochar from agricultural waste and ending with concentration measurement using ultraviolet–visible (UV-vis) spectroscopy. The use of agricultural waste is highlighted due to its high cellulose and organic material content, which facilitates its conversion into carbon. This published guide is intended for researchers and students, offering an accessible and cost-effective method for creating carbon adsorbents for batch adsorption processes. Additionally, the paper aligns with Sustainable Development Goals (SDGs) by addressing environmental and resource sustainability (15).

In 2023 a study was published that developed an authentication method to distinguish green coffee beans from the Cerrado Mineiro region, a coffee-producing region in the northwest section of Minas Gerais in Brazil, which has a Protected Designation of Origin (PDO) certification. Using ultraviolet-visible (UV-vis) spectroscopy measured for 130 green coffee samples, the study employed one-class modeling techniques—soft independent modeling of class analogy (SIMCA), data-driven SIMCA (DD-SIMCA), and one-class partial least squares (OCPLS)—to differentiate Cerrado Mineiro coffee from beans from Caparaó, Mogiana, and Sul de Minas (16). Variable selection through ordered predictors selection (OPS) improved model simplicity and performance. Key variables for differentiation were identified as trigonelline and chlorogenic acids. This published approach can be adapted for authenticating other coffee regions and agricultural products (16).

Fluorescence

A review paper published in 2023 explores the use of optical imaging techniques for sorting agricultural products based on quality parameters like size, shape, color, ripeness, sugar content, and acidity. The paper focuses on the use of fluorescence imaging as a non-destructive method for assessing these attributes. The review covers fluorescence spectroscopy concepts and methodology, and discusses how to select the optimal wavelength for effective fluorescence imaging systems. It highlights the application of fluorescence imaging in detecting peel defects in citrus fruits and identifies future research opportunities for enhancing fluorescence imaging in agricultural quality assessment (17).

Soil management is crucial for sustainable agriculture, requiring regular analysis to measure nutrients and other soil properties. Traditional lab-based soil analysis is costly and time-consuming. A 2023 paper reviews recent advancements in using smartphones for on-site soil quality assessment. It highlights smartphone-based methods for determining key soil parameters, organic matter content, and pollutants. While smartphones offer convenience and ease of use, they have known limitations in precision. The paper emphasizes the growing interest in field-deployable technology, such as smartphones, for rapid and cost-effective soil analysis, suggesting significant potential for this emerging approach. Smartphones are useful for both color, using red, green, blue (RGB) color coordinates, and for fluorescence images analyzed using software for RGB measurement calculations (18).

NMR

NMR profiling, sample georeferencing, and geostatistics are used to assess the spatial variability of metabolic expression in durum wheat fields managed by precision agriculture. NMR analysis has been performed on durum wheat at three growth stages in two locations in the Basilicata region of Italy (19). Geostatistical tools were employed to quantify the spatial variability of metabolites and develop a metabolic index. Metabolic maps generated from this data were successfully compared to evaluate the impacts of soil conditions and farming practices (19).

A 2023 study explored how soil affects the micro-component composition of Nero d'Avola wines using 1H NMR-based metabolomics. Two approaches were used: targeted analysis (TA) for profiling and quantifying specific metabolites, and non-targeted analysis (NTA) for fingerprinting wines and examining hydrogen bond networks. NTA revealed that differences in wines were influenced by both metabolite concentrations and hydrogen bond networks, which affect taste and aroma by altering solute interactions with sensory receptors. The study links these findings to soil properties, offering insights into the terroir effect—indicating how soil characteristics impact wine quality (20).

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