scholarly journals Environmental assessment of soil quality indices using near infrared reflectance spectroscopy

2021 ◽  
Vol 922 (1) ◽  
pp. 012009
Author(s):  
D Devianti ◽  
Sufardi ◽  
S Syafriandi ◽  
A A Munawar
Keyword(s):  
Spectral Data ◽  
Soil Quality ◽  
Environmental Assessment ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Soil Samples ◽  
Infrared Reflectance ◽  
Quality Indices ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance

Abstract The main purpose of this preset study is to assess soil quality indices in form of potassium (K) and phosphorus (P) contents using a non-invasive and environmental friendly approach namely near infrared reflectance spectroscopy. Soil samples were obtained from Aceh Besar district in rice field land-use. Near infrared spectral data of soil samples were acquired and recorded as absorbance in wavelength range from 1000 to 2500 nm. On the other hand, actual P and K were measured using standard laboratory procedures by means of Kjeldahl methods. Spectral data were corrected and pre-treated using mean centering approach and applied to all dataset. Prediction models were developed using principal component regression and validated using leverage cross validation. The results showed that both soil quality indices can be predicted with maximum correlation coefficient (r) of 0.98 and ratio prediction to deviation (RPD) index of 3.47 for P, and r of 0.91, RPD of 2.68 for K respectively. It may conclude that environmental assessment, particularly for soil quality determination can be conducted rapidly and non-invasively using near infrared spectroscopy approach.

scholarly journals Predicting quality, texture and chemical content of yam (Dioscorea alata L.) tubers using near infrared spectroscopy

2021 ◽  
pp. 096703352110075
Author(s):  
Adou Emmanuel Ehounou ◽  
Denis Cornet ◽  
Lucienne Desfontaines ◽  
Carine Marie-Magdeleine ◽  
Erick Maledon ◽  
...  
Keyword(s):  
High Throughput ◽  
Dry Matter ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Quality Traits ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Texture Attributes

Despite the importance of yam ( Dioscorea spp.) tuber quality traits, and more precisely texture attributes, high-throughput screening methods for varietal selection are still lacking. This study sets out to define the profile of good quality pounded yam and provide screening tools based on predictive models using near infrared reflectance spectroscopy. Seventy-four out of 216 studied samples proved to be moldable, i.e. suitable for pounded yam. While samples with low dry matter (<25%), high sugar (>4%) and high protein (>6%) contents, low hardness (<5 N), high springiness (>0.5) and high cohesiveness (>0.5) grouped mostly non-moldable genotypes, the opposite was not true. This outline definition of a desirable chemotype may allow breeders to choose screening thresholds to support their choice. Moreover, traditional near infrared reflectance spectroscopy quantitative prediction models provided good prediction for chemical aspects (R2 > 0.85 for dry matter, starch, protein and sugar content), but not for texture attributes (R2 < 0.58). Conversely, convolutional neural network classification models enabled good qualitative prediction for all texture parameters but hardness (i.e. an accuracy of 80, 95, 100 and 55%, respectively, for moldability, cohesiveness, springiness and hardness). This study demonstrated the usefulness of near infrared reflectance spectroscopy as a high-throughput way of phenotyping pounded yam quality. Altogether, these results allow for an efficient screening toolbox for quality traits in yams.

Sign in / Sign up

Export Citation Format

Share Document