Chemometric Evaluation of Discrimination of Aromatic Plants by Using NIRS, LIBS

Ercioglu, Elif; Velioğlu, Hasan Murat; Boyaci, İsmail Hakkı

doi:10.1007/s12161-018-1145-x

Cited by 12 publications

(6 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This constraint reduces our ability to fully understand the drivers of phenotypic diversity, as well as to identify new and ecologically meaningful axes of phenotypic variation. In this perspective, NIRS allows us to detect a large variety of commonly measured chemical compounds such as phosphorus (P)—a key element of the leaf economics spectrum— and base cations [calcium (Ca), potassium (K), and magnesium (Mg)], and other micronutrients ( Cozzolino et al, 2001 ; Ortiz-Monasterio et al, 2007 ; Galvez-Sola et al, 2015 ; Ercioglu et al, 2018 ; de Oliveira et al, 2019 ; Yu et al, 2019 ; Prananto et al, 2021 ). This opens new avenues to link resource-use strategies with plant elemental composition, fluxes, stoichiometry, and beyond, with nutrient cycling in ecosystems ( Ustin et al, 2004 ).…”

Section: Metabolomics As a New Phenotypic Dimension: Future Perspecti...mentioning

confidence: 99%

A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy

et al. 2022

View full text Add to dashboard Cite

The trait-based approach in plant ecology aims at understanding and classifying the diversity of ecological strategies by comparing plant morphology and physiology across organisms. The major drawback of the approach is that the time and financial cost of measuring the traits on many individuals and environments can be prohibitive. We show that combining near-infrared spectroscopy (NIRS) with deep learning resolves this limitation by quickly, non-destructively, and accurately measuring a suite of traits, including plant morphology, chemistry, and metabolism. Such an approach also allows to position plants within the well-known CSR triangle that depicts the diversity of plant ecological strategies. The processing of NIRS through deep learning identifies the effect of growth conditions on trait values, an issue that plagues traditional statistical approaches. Together, the coupling of NIRS and deep learning is a promising high-throughput approach to capture a range of ecological information on plant diversity and functioning and can accelerate the creation of extensive trait databases.

show abstract

Section: Metabolomics As a New Phenotypic Dimension: Future Perspecti...mentioning

confidence: 99%

A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Regarding fingerprinting of bay laurel EO, the first relevant study was published recently [ 41 ] and was about the development of a Principal Component Analysis (PCA) chemometric model for the discrimination of the plant species based on non-destructive NIR, Raman, and Laser-Induced Breakdown Spectroscopy (LIBS) data. In the same period, Wang et al [ 42 ] reported a rather sophisticated procedure to develop a class model for the prediction of commercial products labeled as “laurel leaves” or “laurel EO” using GC/Q-ToF analytical data and a combination of PCA and Partial Least Squares-Discriminant Analysis (PLS-DA) chemometric methods.…”

Section: Quality and Authenticity Aspectsmentioning

confidence: 99%

Bay Laurel (Laurus nobilis L.) Essential Oil as a Food Preservative Source: Chemistry, Quality Control, Activity Assessment, and Applications to Olive Industry Products

Ordoudi

Papapostolou

Nenadis

et al. 2022

Foods

View full text Add to dashboard Cite

Essential oils (EOs) find application as flavoring agents in the food industry and are also desirable ingredients as they possess preservative properties. The Mediterranean diet involves the use of a lot of herbs and spices and their products (infusions, EOs) as condiments and for the preservation of foods. Application of EOs has the advantage of homogeneous dispersion in comparison with dry leaf use in small pieces or powder. Among them, Laurus nobilis (bay laurel) L. EO is an interesting source of volatiles, such as 1,8-cineole and eugenol, which are known for their preservative properties. Its flavor suits cooked red meat, poultry, and fish, as well as vegetarian dishes, according to Mediterranean recipes. The review is focused on its chemistry, quality control aspects, and recent trends in methods of analysis and activity assessment with a focus on potential antioxidant activity and applications to olive industry products. Findings indicate that this EO is not extensively studied in comparison with those from other Mediterranean plants, such as oregano EO. More work is needed to establish authenticity and activity methods, whereas the interest for using it for the preparation of flavored olive oil or for the aromatization and preservation of table oils must be further encouraged.

show abstract

“…There are many different types of data fusion, and the specific techniques used can vary depending on the application and the data being fused. So far, spectral fusion has shown great promise in a wide range of applications, including the identification of soil components [ 19 , 20 ], the identification of hybrid rice [ 21 ], and the evaluation of aromatic plants [ 22 ]. To our knowledge, there have been no prior attempts to combine LIBS and Vis-NIR for PK assessment in different varieties of organic fertilizers.…”

Section: Introductionmentioning

confidence: 99%

Chemometric Approach Based on Explainable AI for Rapid Assessment of Macronutrients in Different Organic Fertilizers Using Fusion Spectra

et al. 2023

View full text Add to dashboard Cite

Wet chemical methods are usually employed in the analysis of macronutrients such as Potassium (K) and Phosphorus (P) and followed by traditional sensor techniques, including inductively coupled plasma optical emission spectrometry (ICP OES), flame atomic absorption spectrometry (FAAS), graphite furnace atomic absorption spectrometry (GF AAS), and inductively coupled plasma mass spectrometry (ICP-MS). Although these procedures have been established for many years, they are costly, time-consuming, and challenging to follow. This study studied the combination of laser-induced breakdown spectroscopy (LIBS) and visible and near-infrared spectroscopy (Vis-NIR) for the quick detection of PK in different varieties of organic fertilizers. Explainable AI (XAI) through Shapley additive explanation values computation (Shap values) was used to extract the valuable features of both sensors. The characteristic variables from different spectroscopic devices were combined to form the spectra fusion. Then, PK was determined using Support Vector Regression (SVR), Partial Least Squares Regression (PLSR), and Extremely Randomized Trees (Extratrees) models. The computation of the coefficient of determination (R2), root mean squared error (RMSE), and residual prediction deviation (RPD) showed that FUSION was more efficient in detecting P (R2p = 0.9946, RMSEp = 0.0649% and RPD = 13.26) and K (R2p = 0.9976, RMSEp = 0.0508% and RPD = 20.28) than single-sensor detection. The outcomes indicated that the features extracted by XAI and the data fusion of LIBS and Vis-NIR could improve the prediction of PK in different varieties of organic fertilizers.

show abstract

Chemometric Evaluation of Discrimination of Aromatic Plants by Using NIRS, LIBS

Cited by 12 publications

References 37 publications

A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy

A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy

Bay Laurel (Laurus nobilis L.) Essential Oil as a Food Preservative Source: Chemistry, Quality Control, Activity Assessment, and Applications to Olive Industry Products

Chemometric Approach Based on Explainable AI for Rapid Assessment of Macronutrients in Different Organic Fertilizers Using Fusion Spectra

Contact Info

Product

Resources

About