Optimize your harvest through deeper analysis of your soil and fruits
Improve the fermentation stage through better process control and analysis
Refine your customer segmentation and branding with data driven insights
THE PERFECT BLEND OF YOUR ART AND OUR SCIENCE
CAMO Software has worked with leading wine makers and research institutes around the world for many years. Our advanced multivariate data analysis software, The Unscrambler® X, is widely used in the wine industry, from viticulture to enology and marketing.
Our latest solutions give you even more value from your data by helping to trend, monitor, predict and ultimately improve product quality.
Our powerful analytical tools help you address challenges such as understanding the impact of environmental factors that influence fruit properties even before it is harvested, or monitoring and controlling the complexities of the fermentation process to consistently produce the quality of wine you aspire to at lower cost.
With your know-how and our world leading data analysis software and expertise, we’ll help bring your data to life.
It can be hard to see patterns and make sense of complex
data using normal statistics, but the patterns show up
immediately when analyzed with the Unscrambler
Dr Bob Dambergs, Senior Research Scientist, the Australian Wine Research Institute
REAL BUSINESS BENEFITS
- – Analyze fruit and plants during the growing process to determine optimal times to harvest
- – Analyze soil content and tailor fertilization to optimize crop yields
- – Grape rot quantification before and during harvesting
- – Analyze complex data to map and manage vineyard variability
- – Optimizing the fermentation process by better understanding which parameters influence it
- – Minimizing energy use for tank cooling in the fermentation process
- – Monitor and make timely adjustments to reduce abnormal fermentations
- – Process monitoring and control through the ageing process
- – Optimization routines for programming and blending to match orders
- – Deeper analysis of data for better consumer insights to refine your brand strategy
- – Clustering and classification models can be used for improved market segmentation
- – Identify and classify products based on their geographical regions and to assure their authenticity
EXAMPLE APPLICATIONS OF MULTIVARIATE ANALYSIS
Determining the shelf-life of a formulation ingredient for product development
A wine producer client employed sample measurements using a spectrophotometer, in order to measure moisture, pH, lactose, galactose, lactic acid, water activity and calcium and L-a-b appearance values.
The client needed to determine how to pretreat and rearrange raw sample data, detect outliers, build and then test prediction models. Using The Unscrambler® software, the client was able to accurately derive the optimal average shelf life of the formulation ingredient. It determined Y-response time using the intervals between the measurement and first fail date. PLS regression modeling was used to detect outliers, select important chemical parameters and build prediction models.
Quantifying the link between product quality, grape grower and location
A wine producer client observed from data collected over several years that the chemical composition of the grapes relative to region and grower is directly related to wine quality.
The client wanted to identify the consistency in the rankings of the growers (i.e. good growers versus bad growers) over several years and whether the improvement of the scores of a particular vineyard would indicate an improvement in the quality of the grapes coming from that vineyard. The producer also needed to combine this information with chemical data, to investigate how chemical parameters correlate with Average Wine Quality scores and the relationship between chemical parameters and different growing regions.
Using The Unscrambler® software, the client generated grower rankings and consistency using Descriptive Statistic Analysis, evaluated tasters and vineyards with PCA (Principal Component Analysis) and estimated the effect of chemical parameters on wine quality using PLS regression coefficients and correlation loadings.
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