SKU: 82929264565
where to buy prickly pear cactus syrup

where to buy prickly pear cactus syrup Prickly Pear Syrup - Made From Natural Prickly Pear Juice - Cactus

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Description

where to buy prickly pear cactus syrup Prickly Pear Syrup - Made From Natural Prickly Pear Juice - CactusWhat it is: A premium, naturally colored prickly pear cactus syrup made from real prickly pear cactus juice and pure cane sugar, available in multiple sizes (e. g., 23 oz, 35 oz, gallon). Who its for: Home cooks, professional chefs, bartenders, cocktail enthusiasts, and gift buyers seeking an authentic Southwestern syrup. Primary uses: Cocktails (e. g., margaritas, spritzers), dessert toppings, breakfast syrups, glazes, marinades, sodas, and sauces.

What it is: A premium, naturally colored prickly pear cactus syrup made from real prickly pear cactus juice and pure cane sugar, available in multiple sizes (e.g.,  23 oz, 35 oz, gallon). Who it’s for: Home cooks, professional chefs, bartenders, cocktail enthusiasts, and gift buyers seeking an authentic Southwestern syrup.
Primary uses: Cocktails (e.g., margaritas, spritzers), dessert toppings, breakfast syrups, glazes, marinades, sodas, and sauces.
Key ingredients: Prickly pear cactus juice, pure cane sugar, citrus (lemon) for balance, citrus pectin for texture.
Flavor profile: Naturally sweet, light tart notes, vibrant magenta/ruby color — adds both flavor and visual appeal.
Packaging/sizes: Sold in multiple size options to suit individual use, gifting, or commercial purposes.

  • Versatile culinary ingredient: Use as a syrup on pancakes, waffles, oatmeal, desserts, and fruit salads.

  • Perfect for mixology: Ideal for prickly pear margaritas, lemon-drops, mojitos, and spritzers, adding both sweetness and color.

  • Rich natural color & flavor: Derived from real prickly pear cactus juice — no artificial coloring.

  • Gift-worthy Southwest flavor: Unique regional product that makes a memorable gift or souvenir.

Developed in the heart of the Southwest, this Prickly Pear Syrup is crafted from authentic prickly pear cactus juice and pure cane sugar, then gently blended to retain vibrant color and natural flavor. It delivers a lively sweetness with subtle tart notes that elevate beverages and foods alike.

Culinary applications:
Use as a table syrup on pancakes, waffles, French toast, or drizzle over lemon bars, cheesecake, or ice cream for a bold, fruity finish. Blend into yogurt or oatmeal for breakfast with a Southwest twist.

Mixology:
Shake up signature cocktails such as prickly pear margaritas, wine coolers, or lemon-lime spritzers. Stir into soda or sparkling water for a refreshing artisanal drink.

Commercial and gift use:
Available in multiple sizes including larger bottles for restaurants and catering. The rich magenta hue and unique regional character make it stand out on shelves or in curated gift baskets.

Ingredients: Real prickly pear cactus juice, pure cane sugar, lemon, citrus pectin.
Storage: Refrigerate after opening.
Made in small batches for quality and flavor consistency.

 

Q: What is prickly pear syrup made from?
A: This syrup is made from real prickly pear cactus juice combined with pure cane sugar, lemon, and citrus pectin to create a naturally sweet and colorful syrup.

Q: What can I use prickly pear syrup for?
A: It’s excellent in cocktails (like margaritas), sodas, desserts, breakfast dishes, glazes for meats, and sauces.

Q: How should I store it?
A: Refrigerate after opening to maintain freshness and flavor.

Q: Does it contain artificial colors or flavors?
A: No. The vibrant red/magenta color and flavor come naturally from the prickly pear cactus juice.

Q: Are there recommended recipes?
A: Yes — mixologists enjoy it in prickly pear margaritas, lemonades, and wine coolers, and chefs use it as a glaze or dessert topping.

FREE Shipping: Enjoy the convenience of FREE shipping within the USA, making it even easier to enhance your mixology and culinary adventures. FREE shipping USA 
UPS & FedEx only No PO Boxes.

Directions - Refrigerate after opening.

Prickly Pear Drink Recipes

Prickly Pear Margarita Recipe (serves 4)

6 oz. Tequila
4 oz. Triple Sec
4 oz. Lime Juice
2 oz. Prickly Pear Cactus Syrup
1 oz. Orange Juice
Kosher Salt (optional)

To serve on the rocks: Mix ingredients and pour over ice in 4 glasses. To serve blended: Pour 4 cups ice in a blender container, add ingredients and blend. Pour into margarita glass with or without a salt rim and garnish with cactus candy or lime slice.

Prickly Pear Wine Cooler Recipe (Makes 1 - 8oz glass)

1 Scoop of Ice
1 Tablespoon of Prickly Pear Syrup
1/2 glass Chablis Wine
1/2 Glass 7-up

Directions: Fill glass with the ingredients and stir well.

Prickly Pear Lemonade Recipe (Makes 1/2 Gallon)

16 oz. Lemon Juice
3/4 Cup Sugar
1/4 Cup Prickly Pear Cactus Syrup
48 oz. Water

Directions: Pour over ice or blend in a blender.

Prickly Pear Martini Recipe

2 oz. Vodka
1/2 oz. Triple Sec
1 Tbsp. Prickly Pear Syrup
1/2 oz Fresh Lime

Directions: Mix ingredients and pour over ice.

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Carol
Houston, US
★★★★★ 5
Need to read book
Format: Hardcover
The truth about the Native people. THANK YOU Kent for writing this book. We purchased about 12 total.
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Reviewed in the United States on November 24, 2019
W
Walter Echo-Hawk, author of THE SEA OF GRASS.
West Palm Beach, US
★★★★★ 5
Native American history at its best!
Format: Hardcover
Kent Blansett's engrossing story about the life & times of the famed Mohawk activist Richard Oakes is Native American history at its best. I appreciated the well-written context provided about the birth, growth and impact of the Red Power Movement and the pivotal role that social justice activism played in the rise of modern Indian nations in the United States today. This scholarly work helps us understand modern Native America and is a "must-read" for every Native American Studies student and scholar, as well as readers interested in important American social justice movements.
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Reviewed in the United States on April 1, 2019
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Verified Purchase
Par
Whiting, US
★★★★★ 5
Excellent book on ML
Format: Paperback
This is a great book on machine learning. Topics covered are extensive - from beginner level to advanced topics including math behind different algorithms. However, not "all" algorithms are covered. Please go through the table of contents. The first part - 11 chapters - covers machine learning concepts and second part covers advanced topics with Pytorch. There are lots of excellent code and they work!! The quality of the book I received is excellent. I have gone through all 742 pages, and it has held up very well!! I used Jupyter notebook to run all examples. I created a new notebook and copied and pasted the code and ran them. This approach worked very well for me. At the same time, I could experiment with my take on the code snippets and definitely added to my knowledge. Only issue I have is on the second part of the book discussing PyTorch: (1) Some packages are a bit older version: e.g., transformer 4.9.1 whereas current version is 4.48+. It took some tweaking/recoding to get the examples working. (2) There is not much discussion on why certain architecture was chosen - e.g., number of layers, is there a rule of thumb on how to improve performance by changing these parameters? Even with CUDA the code run for a long time. Therefore, experimenting with different values of parameters become too time consuming. (3) On the same note, if I can achieve test accuracy of 90%+ using logistic regression and almost the same (perhaps one or two percent better with PyTorch with IMDB movie review dataset and that two much faster why should I use PyTorch for this dataset? Obviously, PyTorch is for certain types of problems. Discussions can be included by not adding to the exhaustive (and apt) contents. Personally I was disappointed by lack of any example on time series. Must have for ML practitioner as a reference and guide.
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Reviewed in the United States on December 20, 2024
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Verified Purchase
Richard Hackathorn
Natrona Heights, US
★★★★★ 5
Excellent Textbook for Hands-On Learning of ML
Format: Kindle
This textbook is for the serious life-long learners of machine learning. There are at least two ways to ‘consume’ this book. For the expert in ML, this is a textbook to study as a clear comprehensive ML overview and then to dive into sections of interest or ignorance. The concepts are grounded in code examples and are well cited (with links) to sources. Further, this textbook is appropriate if you are TensorFlow-centric and want to broaden into cutting-edge ML models/tools coded in PyTorch. For a new learner to ML, this is a textbook to DO (not just READ) with hands-on and brain-engaged. If you realize that ML is a key life-long skill for your career, consider this textbook as part of a daily learning habit (10-30 min). From personal experience, my advice to the new learner is as follows… First, clone the GitHub repository, setup your Python environment, and study the textbook, while working through the notebooks. Go on tangents and break the code. Do this methodically as part of your daily learning habit, but do not hesitate to jump ahead several chapters to prepare for tomorrow’s meeting. There is enough excellent material here for a full year of ML adventures. I did a similar strategy with Raschka’s first textbook. About four years ago, I had finished Andrew Ng’s Deep Learning Specialization as a student in his first cohort. I knew the concepts well but could not do the actual application coding. I was surprised how my Python coding improved by following Raschka’s clean and elegant style. And Raschka’s code examples were meaty enough to be springboards into working applications. Several textbook editions later, what is different about this new edition? First, it moves you through scikit-Learn (a firm foundation) to PyTorch, instead of TensorFlow. PyTorch is a better stepping-stone, both conceptually and practically. With PyTorch, you will go further with less energy, while being able to convert your efforts into TensorFlow as needed. In addition, most of the cutting-edge ML/AI/DL research is in PyTorch. It is nice to read a recent arXiv paper, clone their repository, click on the Colab tutorial, and replicate their experiments, along with picking up a ton of new coding tricks & tips. I am excited to work through these PyTorch sections to hone my skills. Second, there is a clear recognition of model tracking and tuning practices. This is often a gap in other ML textbooks and courses. Once you progress beyond the simple demo examples in a lecture, you realize that the real work is experiments, more experiments, and still more experiments, so that you must understand what the model architecture and hyperparameters are doing to your dataset. There is good coverage of scikit-Learn pipeline, grid search, model performance, and the like. Third, ML/AI/DL practice is rapidly evolving. Every week new ML packages/services become available that could save much grief on your current project. What is refreshing about Raschka’s textbook series is that he constantly adding cutting-edge topics because he likes to stay current and to help us stay current. Hence, this edition contains recent ML treats as: transformers, self-supervised learning, autoencoders-to-GAN, graph neural networks, DBSCAN, t-SNE (with brief mention of UMAP), and PyTorch-Lightning.
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Reviewed in the United States on February 26, 2022
A
Verified Purchase
Amazon Customer
Grantham, US
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
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Reviewed in the United States on December 10, 2025

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