Spectroscopy and Chemometrics News Weekly #35, 2019

Near Infrared (NIR)

“Evaluation of diesel exhaust fluid (DEF) using near-infrared spectroscopy and multivariate calibration” LINK

“A weighted ensemble method based on wavelength selection for near-infrared spectroscopic calibration” LINK

“110th Anniversary: Real-Time Endpoint Detection of Fluidized Bed Drying Process Based on a Switching Model of Near-Infrared Spectroscopy” LINK

“Supervised Dictionary Learning with Regularization for Near-infrared Spectroscopy Classification” tobacco NIRS LINK

“Evaluation of NIRS as non-destructive test to evaluate quality traits of purple passion fruit” LINK

“A Comparison of Sparse Partial Least Squares and Elastic Net in Wavelength Selection on NIR Spectroscopy Data” LINK

“High-throughput analysis of leaf physiological and chemical traits with VIS-NIR-SWIR spectroscopy: a case study with a maize diversity panel.” LINK

“Comparative study on the use of three different near infrared spectroscopy recording methodologies for varietal discrimination of walnuts” LINK

“Support vector machine regression on selected wavelength regions for quantitative analysis of caffeine in tea leaves by near infrared spectroscopy” LINK

“Use of near infrared spectroscopy and spectral database to assess the quality of pharmaceutical products and aid characterization of unknown components” LINK

“Determination of soil organic matter using visible-near infrared spectroscopy and machinelearning” LINK

“Evaluation of acetic acid and ethanol concentration in a rice vinegar internal venturi injector bioreactor using Fourier transform near infrared spectroscopy” LINK


“Determination of the superficial citral content on microparticles: An application of NIR spectroscopy coupled with chemometric tools” LINK

“Non-Invasive Tools to Detect Smoke Contamination in Grapevine Canopies, Berries and Wine: A Remote Sensing and Machine Learning Modeling Approach” LINK

“Optimization of soluble solids content prediction models in ‘Hami’melons by means of Vis-NIR spectroscopy and chemometric tools” LINK

“Fast quantitative detection of Black Pepper and Cumin adulterations by near-infrared spectroscopy and multivariate modeling” LINK

“Prediction of toughness and other beef quality characteristics of the m. longissimus thoracis using polarized near-infrared reflectance spectroscopy” LINK

“Application of near infrared for on-line monitoring of heavy fuel oil at thermoelectric power plants. Part I: Development of chemometric models” LINK

What’s a Near-Infrared-sensor for combines? Donau Soja collects data from soya fields for the soya yield- and protein prediction model within CYBELE_H2020. The NIR sensor measures & maps protein, oil and other quality parameters in real time during the upcoming harvest season! LINK

“NIRs calibration models for chemical composition and fatty acid families of raw and freeze-dried beef: a comparison” LINK

“Honey botanical origin classification using hyperspectral imaging and machine learning” LINK


Spectroscopy and Chemometrics News Weekly 34, 2019 | NIRS NIR Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Software Sensors QAQC Testing Quality Checking LabManagers laboratory digitalization LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 34, 2019 | NIRS NIR Spektroskopie Chemometrie Spektrometer Sensor Nahinfrarot Chemie Analytik Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse Qualitätslabor labdata LINK

Spettroscopia e Chemiometria Weekly News 34, 2019 | NIRS NIR Spettroscopia Chemiometria analisi chimica Spettrale Spettrometro Chem Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem prediction models LINK


Most people now know companies like Google & Facebook collect & sell your data. Yet some people still think, “So what? I have nothing to hide.” Here’s five compelling reasons to tell them why your privacy is worth more than you think! LINK


“Relevance of a near infrared spectral index for assessing tillage and fertilization effects on soil water retention” LINK


“Fast And Simultaneous Prediction Of Agricultural Soil Nutrients Content Using Infrared Spectroscopy” LINK

“Quantitative and qualitative phenotyping of disease resistance of crops by hyperspectral sensors: seamless interlocking of phytopathology, sensors, and machine learning is needed!” LINK

“Prediction of macronutrients in plant leaves using chemometric analysis and wavelength selection” LINK


“Spectroscopic data supporting investment decisions” LINK


Proof of Concept and Work

In 2021 we automatically calibrated Mango DM with RMSEP = 0.7247 with our software
that performs better than the one in an research paper that states :
  • “Readers are encouraged to use this big data set and produce innovative ideas and algorithms to achieve RMSEP better than 0.79%.”
See Mango DM – dry matter prediction in mango fruit with near-infrared (NIR) spectroscopy

Chemometric software competitions (aka shootouts) are a good way to check algorithms, software and knowledge against all other experts in the field.

Imagine that the prediction results can be produced with any kind of software and newest algorithms.

And we just use PLS right to generate models that can be used on all NIR software systems, because PLS is a quasi standard, supported in all major chemometrics software.

Our software framework reached very good results, got gold (rank #1) and silver (rank #2) during well known international NIR Chemometric software shootouts* so far, the competitions are held bi-annual.

Rank / competitors  Competition / Conference  Year
 #1 / 1  **  Kaji / ANSIG  2014
 #1 / 150  Kaji / ANSIG  2012
 #2 / ???  IDRC / IDRC  2012
The Kaji Competition

A set of NIR spectral data will be available for downloading from the ANISG website and contestants will be asked to find and explain a “best” chemometric model to robustly predict samples of the same type.
A panel will select the three “best” entries based on the predicted results and spectroscopic explanation of the products and attributes of interest. 


The IDRC Competition

The Software Shootout has been a staple of the IDRC. It is a competition amongst participants of the conference that aims at determining the person who developed the best model and obtained the lowest prediction error for a particular problem.
Every IDRC, a new challenge is proposed to participants. The challenge consists of a data set with calibration, test and a validation set.
Participants are given target values for the calibration and test sets but must do their best to develop a model that will predict the validation set as accurately and precisely as possible. Challenges from all sorts of fields of NIRS have been used (agriculture, biomedical, pharmaceutical, soil, …).


*) The author was unable to present the results at the conferences, so this ranking was not official but confirmed by the shootout organizers. Thanks go to Benoit Igne, IDRC 2012 shootout organizer and Steve Holroyd, Kaji Competition organizer at ANISG Conference 2012.  


Our chemometric software framework can significantly reduce the time spent for NIR method development and fine optimization. The time saving can be achieved through highly automated experiments and the usage of cloud computing. Calibrations are built and evaluated using automated good practices protocols resulting in useful, precise and robust Calibrations. The high number of experiments enables a deep screening of the solution domain to find the optimum calibration settings, something currently unavailable in standard chemometric software.

**) We were the only participator that got the 4 competition tasks (4-times more than usual) completed in that short time and submitted the fully documented results. After the competition, the information was given, that the data was originated from forages and the constituents were dry matter, organic matter digestibility, protein and ash. Thanks go to Daniel Cozzolino, Kaji 2014 Competition organizer.

Meet us at the NIR 2013 – 16th International Conference on Near Infrared Spectroscopy (ICNIRS 2013)

Meet us at the NIR 2013 – 16th International Conference on Near Infrared Spectroscopy (ICNIRS 2013) in Montpeiller, France , 2-7 June 2013. If you are interested in analysis and optimization of your data during the conference, please take a JCAMP export of your data on a USB-memory stick with you. We will also present a poster P129 : ‘A novel intelligent knowledge-based Chemometric Software Framework for quantitative NIR Calibration Modeling‘ by Roman Bossart   some links : La Grande Motte Panorama, WebCam, Weather