Related Experiment Video
Updated: May 12, 2026

Qualitative Identification of Carboxylic Acids, Boronic Acids, and Amines Using Cruciform Fluorophores
Published on: August 19, 2013
CIEL*a*b* color space predictive models for colorimetry devices--analysis of perfume quality
Rabia Korifi1, Yveline Le Dréau, Jean-François Antinelli
1Laboratoire LISA, EA 4672 Equipe METICA, Case 451, Aix-Marseille Université, 13397 Marseille Cedex 20, France. rabia.korifi@yahoo.fr
Abstract:
Color perception plays a major role in the consumer evaluation of perfume quality. Consumers need first to be entirely satisfied with the sensory properties of products, before other quality dimensions become relevant. The evaluation of complex mixtures color presents a challenge even for modern analytical techniques. A variety of instruments are available for color measurement. They can be classified as tristimulus colorimeters and spectrophotometers. Obsolescence of the electronics of old tristimulus colorimeter arises from the difficulty in finding repair parts and leads to its replacement by more modern instruments. High quality levels in color measurement, i.e., accuracy and reliability in color control are the major advantages of the new generation of color instrumentation, the integrating sphere spectrophotometer. Two models of spectrophotometer were tested in transmittance mode, employing the d/0° geometry. The CIEL(*)a(*)b(*) color space parameters were measured with each instrument for 380 samples of raw materials and bases used in the perfume compositions. The results were graphically compared between the colorimeter device and the spectrophotometer devices. All color space parameters obtained with the colorimeter were used as dependent variables to generate regression equations with values obtained from the spectrophotometers. The data was statistically analyzed to create predictive model between the reference and the target instruments through two methods. The first method uses linear regression analysis and the second method consists of partial least square regression (PLS) on each component.
More Related Videos
10:25Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
Published on: June 28, 2016
07:05Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
Published on: June 18, 2021