Enzymatic test for a semi-quantitative determination of total hardness, pH, nitrites, nitrates and chlorine in water.
5-IN-1 WATER TEST
Test principle
Enzymatic test strips are intended for quick visual semi-quantitative determination of total hardness, pH, nitrites, nitrates and chlorine in water. One test strip is intended for testing of single water sample.
Intended use
Highly recommended to test fruits and vegetables washing water, rinse water from production of food industry, for beverages producers (beer, juices, soft drinks, wine, soups, etc.), tap water, producers and suppliers of RO (reverse osmosis) water, well / borehole water, fish farms, farms and hydroponics cultures, swimming pools, aquariums, etc.
Format
50 strips tube
Extremely easy procedure![](data:image/png;base64,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)
1. Dip the strip into the water sample. All the sensor pads should be submerged.
2. After 2–3 sec., take the strip out and remove excess of water by shaking it off or by careful touch of the strip edge to a clean filter paper (paper tissue).
3. Place the test strip onto an even clean dry surface with sensor pads up. After two minutes compare color of each sensor pad with a relevant zone on the color scale*:
![](/adm/_kcfinder/upload/images/ScreenHunter_116%20Jun.%2009%2009.35.jpg)
Normal ranges for each parameter are determined by state or local sanitary authorities.
*The range limits shown on the color scale are for reference only.
Test Sensitivity
Parameter |
Determination Range |
Sensitivity/Accuracy |
|
Nitrates (NO3-) |
10 mg/l –≥ 250 mg/l |
0.5 mg/l |
|
Nitrites (NO2-) |
0.5 mg/l –≥ 10 mg/l |
0.5 mg/l |
|
Total hardness |
4°d–16°d (1°d = 0.3566 mg-eq/l) |
4°d (1.5 mg-equivalent/liter) |
|
Free Chlorine |
1 mg/l – 20 mg/l |
1 mg/l |
|
pH |
5–9 pH units |
0.5 pH units |
|
Kit |
Reference |
Format |
5-in-1 Water Test |
KT-6123 |
50 strips |
|